4194869820260408202604082624-821292026Frontiers in artificial intelligenceFront Artif IntellNavigating opportunities and challenges of generative AI in higher education: towards responsible, equitable, and human-centered integration.17509781750978175097810.3389/frai.2026.1750978The rapid emergence of generative AI (GenAI) tools, such as ChatGPT, presents transformative potential for higher education (HE) while also raising significant ethical, pedagogical, and policy- related concerns. This systematic and thematic review, based on 46 documents from the Web of Science (WoS), narrows its qualitative analysis to 27 peer-reviewed articles to explore key trends in implementation. Findings highlight GenAI's benefits in improving personalized learning, self-regulated strategies, and adaptive feedback. However, critical issues persist, particularly regarding academic integrity, data privacy, equity, and responsible governance. Core themes include AI literacy, technology acceptance, ethical oversight, and the imperative for culturally responsive integration. The study underscores the need for stakeholder collaboration to ensure the ethical and effective integration of GenAI in education. When applied thoughtfully, GenAI holds significant promise in reshaping educational practices while safeguarding fundamental academic values.Copyright © 2026 Coman, Gherheș, Bucs, Coman, Victoria-Rodica, Bódi and Șerbănescu.ComanClaudiuCFaculty of Sociology and Communication, Transylvania University of Brasov, Brașov, Romania.The Academy of Romanian Scientists, Bucharest, Romania.GherheșVasileVDepartment of Communication and Foreign Languages, Politehnica University of Timisoara, Timisoara, Romania.BucsAnnaADepartment of Sociology and Social Sciences, University of Craiova, Craiova, Romania.ComanEcaterinaEFaculty of Economics and Business Administration, Transylvania University of Brasov, Brașov, Romania.Victoria-RodicaCiocaCFaculty of Psychology and Educational Sciences, Babeș-Bolyai University, Calea Moților, Cluj-Napoca, Romania.BódiDiana-CristinaDCFaculty of Sociology and Communication, Transylvania University of Brasov, Brașov, Romania.The Academy of Romanian Scientists, Bucharest, Romania.ȘerbănescuRaluca-MariaRMFaculty of Sociology and Communication, Transylvania University of Brasov, Brașov, Romania.The Academy of Romanian Scientists, Bucharest, Romania.Faculty of Economics and Business Administration, Transylvania University of Brasov, Brașov, Romania.engJournal Article20260323SwitzerlandFront Artif Intell1017705512624-8212AI literacyGenAIacademic integrityhigher educationhuman-centered AIpoliciesregulationsregulatory gapsThe author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.2025112020262192026227202648121720264812162026484332026323epublish41948698PMC1305089510.3389/frai.2026.1750978Abdel Baky N. (2022). The effectiveness of an artificial intelligence based learning environment in developing academic achievement, decision-making skills and attitude towards technology among the female students of the College of Education at King Khalid University considering of Kolb's model. J. Educ. Sohag Univ. 96, 1–45. doi: 10.21608/edusohag.2022.22891010.21608/edusohag.2022.228910Abuzakiyeh K. (2018). Expert systems in artificial intelligence and their employment in education and education. J. Fac. Educ. Univ. Al-Zawiya 12, 1–45.Ali H., Aysan A. F. (2025). Ethical dimensions of generative AI: a cross-domain analysis using machine learning structural topic modeling. Int. J. Ethics Syst. 41, 3–34. doi: 10.1108/IJOES-04-2024-011210.1108/IJOES-04-2024-0112Almatrafi O., Johri A., Lee H. (2024). A systematic review of AI literacy conceptualization, constructs, and implementation and assessment efforts (2019–2023). Comput. Educ. Open 6:100173. doi: 10.1016/j.caeo.2024.10017310.1016/j.caeo.2024.100173Almulla M. A. (2024). Investigating influencing factors of learning satisfaction in AI ChatGPT for research: university students’ perspective. Heliyon 10, 2–18. doi: 10.1016/j.heliyon.2024.e32220,10.1016/j.heliyon.2024.e32220PMC1120029638933954Alves De Castro C., Carthy A. (2021). Social Media Influencers (SMIs) in Context: A Literature Review. figshare. J. contri. Dublin. doi: 10.6084/m9.figshare.19673517.v110.6084/m9.figshare.19673517.v1Alzahrani A. (2022). A systematic review of artificial intelligence in education in the arab world. Rev. Amazon. investig, 11, 293–305.Amoozadeh M., Nam D., Prol D., Alfageeh A., Prather J., Hilton M., et al. (2024). Student-AI Interaction: A case Study of CS1 Students, In Proceedings of the 24th Koli Calling International Conference on Computing Education Research. Koli, Finland. 1–13.Anders A. D., Speltz E. D. (2025). Developing generative AI literacies through self-regulated learning: a human-centered approach. Comput. Educ. Artif. Intell. 9:100482. doi: 10.1016/j.caeai.2025.10048210.1016/j.caeai.2025.100482Apolzan D. I., Cîmpineanu D. M.-J. (2024) Benefits and challenges of using artificial intelligence in education. Euro-Atl. Resil. J., 2:49-72.Ateeq A., Alzoraiki M., Milhem M., Ateeq R. A. (2024). Artificial intelligence in education: implications for academic integrity and the shift toward holistic assessment. Front. Educ. 9:1470979. doi: 10.3389/feduc.2024.147097910.3389/feduc.2024.1470979Bai J. Y., Zawacki-Richter O., Muskens W. (2024). Re-examining the future prospects of artificial intelligence in education in light of the GDPR and ChatGPT. Turk. Online J. Distance Educ. 25, 20–32. doi: 10.17718/tojde.124890110.17718/tojde.1248901Barus O., Hidayanto A. N., Eitiveni I. (2025). Mapping generative AI’S ethical issues in higher education: a felt-guided systematic review [PEMETAAN ISU ETIKA generative AI di PENDIDIKAN TINGGI: TINJAUAN SISTEMATIS BERPANDUAN felt]. Polyglot J. Ilm. 21, 159–196. doi: 10.19166/pji.v21i2.1002010.19166/pji.v21i2.10020Bengio Y., Clare S., Prunkl C., Rismani S., Andriushchenko M., Bucknall B., et al. (2025). International AI safety report 2025: first key update: capabilities and risk implications. (arXiv:2510.13653). arXiv, 2–31. doi: 10.48550/arXiv.2510.1365310.48550/arXiv.2510.13653Bewersdorff A., Seßler K., Baur A., Kasneci E., Nerdel C. (2023). Assessing Student errors in Experimentation using artificial Intelligence and large language models: A comparative study with human raters. Computers and Education: Artificial Intelligence 5:100177. doi: 10.1016/j.caeai.2023.10017710.1016/j.caeai.2023.100177Bhullar P. S., Joshi M., Chugh R. (2024). ChatGPT in higher education: a synthesis of the literature and a future research agenda. Educ. Inf. Technol. 29, 21501–21522. doi: 10.1007/s10639-024-12723-x10.1007/s10639-024-12723-xBirenbaum M. (2023). The chatbots’ challenge to education: disruption or destruction? Educ. Sci. 13:711. doi: 10.3390/educsci1307071110.3390/educsci13070711Bishop L. (2023). A computer wrote this paper: what ChatGPT means for education, research, and writing. Res, and Wri. (January 26, 2023).Braun V., Clarke V. (2006). Using thematic analysis in psychology. Qual. Res. Psychol. 3, 77–101. doi: 10.1191/1478088706qp063oa10.1191/1478088706qp063oaBroinowski A., Martin F. R. (2024). Beyond the deepfake problem: benefits, risks and regulation of generative AI screen technologies. Media Int. Aust.:1329878X241288034. doi: 10.1177/1329878X24128803410.1177/1329878X241288034Chang D. H., Lin M. P.-C., Hajian S., Wang Q. Q. (2023). Educational design principles of using AI chatbot that supports self-regulated learning in education: goal setting, feedback, and personalization. Sustainability 15:12921. doi: 10.3390/su15171292110.3390/su151712921Chen V. Y., Lin P. M. (2024). The power of nostalgic emotion: how Hong Kong traditional tong sui influences generation z’s purchase intentions. Br. Food J. 126, 3197–3220. doi: 10.1108/bfj-01-2024-000610.1108/bfj-01-2024-0006Chen A., Xiang M., Zhou J., Jia J., Shang J., Li X., et al. (2025). Unpacking help-seeking process through multimodal learning analytics: a comparative study of ChatGPT vs human expert. Comput. Educ. 226:105198. doi: 10.1016/j.compedu.2024.10519810.1016/j.compedu.2024.105198Coelho R., Bjune A. E., Ellingsen S., Solheim B. M., Thormodsaeter R., Wasson B., et al. (2025). A call for clarity: biology students advocate for guidelines for the use of generative AI in higher education. J. Sci. Educ. Technol. 34, 853–865. doi: 10.1007/s10956-025-10216-110.1007/s10956-025-10216-1Conceição J. M. C., van der Stappen E. (2025). The impact of AI on inclusivity in higher education: a rapid review. Educ. Sci. 15:1255. doi: 10.3390/educsci1509125510.3390/educsci15091255Crompton H., Edmett A., Ichaporia N., Burke D. (2024). AI and English language teaching: affordances and challenges. Br. J. Educ. Technol. 55, 2503–2529. doi: 10.1111/bjet.1346010.1111/bjet.13460de Silva D., Kaynak O., El-Ayoubi M., Mills N., Alahakoon D., Manic M. (2024). Opportunities and challenges of generative artificial intelligence: research, education, industry engagement, and social impact. IEEE Ind. Electron. Mag. 19, 30–45.Dellermann D., Ebel P., Söllner M., Leimeister J. M. (2019). Hybrid intelligence. Bus. Inf. Syst. Eng. 61, 637–643. doi: 10.1007/s12599-019-00595-210.1007/s12599-019-00595-2El-hajj W. (2019) The educators’ role after AI. Innovation Arabia 12 Proceedings, 18Elnagar M. H., Yadav S., Venugopalan S. R., Lee M. K., Oubaidin M., Rampa S., et al. (2024). ChatGPT and dental education: opportunities and challenges. Semin. Orthod. 30, 401–404. doi: 10.1053/j.sodo.2024.03.00410.1053/j.sodo.2024.03.004Evangelista E. D. L. (2025). Ensuring academic integrity in the age of ChatGPT: rethinking exam design, assessment strategies, and ethical AI policies in higher education. Contemp. Educ. Technol. 17:ep559. doi: 10.30935/cedtech/1577510.30935/cedtech/15775Fecher B., Hebing M., Laufer M., Pohle J., Sofsky F. (2025). Friend or foe? Exploring the implications of large language models on the science system. AI & Soc. 40, 447–459.Figueroa De La Fuente M., Farhadian G. (2025). Systematic review of the impact of artificial intelligence in higher education. J. Teach. Learn. 19, 72–96. doi: 10.22329/jtl.v19i4.984910.22329/jtl.v19i4.9849Foucault M. (1982). The subject and power. Crit. Inq. 8, 777–795. doi: 10.1086/44818110.1086/448181Foucault M. (2001). The Order of Things: Archaeology of the Human Sciences. London: Routledge. doi: 10.1007/978-1-137-05194-3_510.1007/978-1-137-05194-3_5Fuchs A., Passarella A., Conti M. (2022). Demonstrating Optimized Delegation Between AI and Human Agents, In 2022 IEEE International Conference on Smart Computing (SMARTCOMP). Helsinki, Finland: IEEE. 171–173.Garcia-López I. M., Trujillo-Liñán L. (2025). Ethical and regulatory challenges of generative AI in education: a systematic review. Front. Educ. 10:1565938. doi: 10.3389/feduc.2025.156593810.3389/feduc.2025.1565938Geyer W., Weisz J., Pinhanez C. S., Daly E. (2022). What is human-centered AI. IBM Res. Blog:31.Gherheş V. (2018). Why are we afraid of artificial intelligence (AI)? Eur. Rev. Appl. Sociol. 11:17. doi: 10.1515/eras-2018-000610.1515/eras-2018-0006Gherheș V., Fărcașiu M. A., Cernicova-Buca M., Coman C. (2025). AI vs. human-authored headlines: evaluating the effectiveness, trust, and linguistic features of ChatGPT-generated clickbait and informative headlines in digital news. Information 16:2. doi: 10.3390/info1602015010.3390/info16020150Giannakos M., Azevedo R., Brusilovsky P., Cukurova M., Dimitriadis Y., Hernandez-Leo D., et al. (2024). The promise and challenges of generative AI in education. Behav. Inf. Technol. 44, 2518–2544. doi: 10.1080/0144929x.2024.239488610.1080/0144929x.2024.2394886Gupta P., Mahajan R., Badhera U., Kushwaha P. S. (2024). Integrating generative AI in management education: a mixed-methods study using social construction of technology theory. Int. J. Manag. Educ. 22:101017. doi: 10.1016/j.ijme.2024.10101710.1016/j.ijme.2024.101017Haneya H., AlKaf D., Bajammal F., Brahimi T. (2021). A meta-analysis of artificial intelligence applications for tracking COVID-19: the case of the UAE. Procedia Comput. Sci. 194, 180–189. doi: 10.1016/j.procs.2021.10.072,10.1016/j.procs.2021.10.072PMC864130134876933He Q., You X., Shen J. (2025). AI-generated content and academic norms: perspectives and reflections of college students. Interact. Learn. Environ., 1–15.Hughes L., Malik T., Dettmer S., Al-Busaidi A. S., Dwivedi Y. K. (2025). Reimagining higher education: navigating the challenges of generative AI adoption. Inf. Syst. Front., 1–23.Iasechko S., Pereiaslavska S., Smahina O., Lupei N., Mamchur L., Tkachova O. (2022). Artificial intelligence in the modern educational space: problems and prospects. Int. J. Comput. Sci. Netw. Secur. 22, 25–32.Jin F., Lin C.-H., Lai C. (2025). Modeling AI-assisted writing: how self-regulated learning influences writing outcomes. Comput. Hum. Behav. 165:108538. doi: 10.1016/j.chb.2024.10853810.1016/j.chb.2024.108538Johnson N. N., Johnson T. L., McCurdy D. (2024) Artificial Intelligence, Academic Freedom, and the Evolving Debate Over Forgery and Truth in the Twenty-first Century. AAUP J. Acad. Freedom.Karran A. J., Charland P., Trempe-Martineau J., de Ortiz Guinea Lopez Arana A., Lesage A.-M., Sénécal S., et al. (2025). Multi-stakeholder perspective on responsible artificial intelligence and acceptability in education. NPJ Sci. Learn. 10:44. doi: 10.1038/s41539-025-00333-2,10.1038/s41539-025-00333-2PMC1223822440628752Khosravi H., Shibani A., Jovanovic J., Pardos Z. A., Yan L. (2025). Generative AI and learning analytics: Pushing boundaries, preserving principles. Learn. Anal, 12, 1–11.Kickbusch S., Ashford-Rowe K., Kemp A., Boreland J., Huijser H. (2025). Beyond detection: redesigning authentic assessment in an AI-mediated world. Educ. Sci. 15:1537. doi: 10.3390/educsci1511153710.3390/educsci15111537Kim Y., Kim H., Ahn H. (2022). A study on AI literacy of university students in language and language education. J. Korea Contents Assoc. 23, 165–174. doi: 10.5392/JKCA.2023.23.01.16510.5392/JKCA.2023.23.01.165Kofinas A. K., Tsay C. H.-H., Pike D. (2025). The impact of generative AI on academic integrity of authentic assessments within a higher education context. Br. J. Educ. Technol. 56, 2522–2549. doi: 10.1111/bjet.1358510.1111/bjet.13585Kohnke L., Zou D., Xie H. (2025). Microlearning and generative AI for pre-service teacher education: a qualitative case study. Educ. Inf. Technol. 30, 21221–21248. doi: 10.1007/s10639-025-13606-510.1007/s10639-025-13606-5Kong S.-C., Lee J. C.-K., Tsang O. (2024). A pedagogical design for self-regulated learning in academic writing using text-based generative artificial intelligence tools: 6-P pedagogy of plan, prompt, preview, produce, peer-review, portfolio-tracking. Res. Pract. Technol. Enhanc. Learn. 19, 1–18. doi: 10.58459/rptel.2024.1903010.58459/rptel.2024.19030Krvavica A., Papić A., Mićunović M. (2025). Students’ attitudes towards the opportunities and challenges of generative artificial intelligence in the academic environment. TEM J. 14, 1520–1530. doi: 10.18421/tem142-5110.18421/tem142-51Kumar P. C., Cotter K., Cabrera L. Y. (2024). Taking responsibility for meaning and mattering: An agential realist approach to generative AI and literacy. Read. Res. Q, 59, 570–578.Kurzweil R. (2005). The Singularity is Near: When Humans Transcend Biology. Viking. London, UK: Palgrave Macmillan.Lazorko O., Zhanna V., Yahupov V., Valchuk-Orkusha O., Melnyk I., Sherman M. (2021). The safety of professionalization subjects in psychological and neuropsychological aspects. Brain 12, 19–39. doi: 10.18662/brain/12.1/16810.18662/brain/12.1/168Lee Y.-J., Oh J., Hong C. (2024). Exploratory research on understanding university students’ artificial intelligence literacy in a Korean university. Online J. Commun. Media Technol. 14:e202440. doi: 10.30935/ojcmt/1471110.30935/ojcmt/14711Lia H., Atkinson A. G., Navarro S. M. (2024). Cross-industry thematic analysis of generative AI best practices: applications and implications for surgical education and training. Glob. Surg. Educ. 3:61.Machleidt P., Mráčková J., Mráček K. (2024). Perception of the risks inherent in new AI technologies. J. Technol. Assess. Theory Pract. 33, 42–48. doi: 10.14512/tatup.33.2.4210.14512/tatup.33.2.42Miao F., Giannini S., Holmes W. (2023). Guidance for Generative AI in Education and Research. Paris, France: UNESCO.Mittelstadt B. D. (2025). Europe’s fair AI opportunity. AIRe, 2.Mollen J. (2025). LLMs beyond the lab: the ethics and epistemics of real-world AI research. Ethics Inf. Technol. 27, 1–11.Newton P. M., Jones S. (2025). Education and training assessment and artificial intelligence: a pragmatic guide for educators. Br. J. Biomed. Sci. 81:14049. doi: 10.3389/bjbs.2024.14049,10.3389/bjbs.2024.14049PMC1183777639973890Ng S.-L., Ho C.-C. (2025). Generative AI in education: mapping the research landscape through bibliometric analysis. Information 16:657. doi: 10.3390/info1608065710.3390/info16080657Ng D. T. K., Tan C. W., Leung J. K. L. (2024). Empowering student self-regulated learning and science education through ChatGPT: a pioneering pilot study. Br. J. Educ. Technol. 55, 1328–1353.Nguyen A., Gul F., Dang B., Huynh L., Tuunanen T. (2025). Designing embodied generative artificial intelligence in mixed reality for active learning in higher education. Innov. Educ. Teach. Int. 62, 1632–1647. doi: 10.1080/14703297.2025.249917710.1080/14703297.2025.2499177Nutbeam D., Milat A. J. (2025). Artificial intelligence and public health: prospects, hype and challenges. Public Health Res. Pract. 35:PU24001. doi: 10.1071/PU24001,10.1071/PU2400140443074Pikhart M., Al-Obaydi L. H. (2025). Reporting the potential risk of using AI in higher education: subjective perspectives of educators. Comput. Hum. Behav. Rep. 18:100693. doi: 10.1016/j.chbr.2025.10069310.1016/j.chbr.2025.100693Pinho I., Costa A. P., Pinho C. (2025). Generative AI governance model in educational research. Front. Educ. 10:1594343. doi: 10.3389/feduc.2025.159434310.3389/feduc.2025.1594343Polonioli A., Ghioni R., Greco C., Juneja P., Tagliabue J., Watson D., et al. (2023). The ethics of online controlled experiments (A/B Testing). Minds Mach., 33, 667–693.Prasad P., Sane A. (2024). A Self-Regulated Learning Framework using Generative AI and its Application in CS Educational Intervention design, In Proceedings of the 55th ACM Technical Symposium on Computer Science Education. Portland, USA. 1, 1070–1076.Ryan M. (2024). We’re only human after all: a critique of human-centred AI. AI & Soc. 40, 1303–1319. doi: 10.1007/s00146-024-01976-2,10.1007/s00146-024-01976-2PMC1198556340225308Salhab R., Aboushi M. M. (2025). Influence of AI literacy and 21st-century skills on the acceptance of generative artificial intelligence among college students. Front. Educ. 10:1640212. doi: 10.3389/feduc.2025.164021210.3389/feduc.2025.1640212Sauvola J., Tarkoma S., Klemettinen M., Riekki J., Doermann D. (2024). Future of software development with generative AI. Autom. Softw. Eng. 31:26. doi: 10.1007/s10515-024-00426-z10.1007/s10515-024-00426-zSelwyn N. (2024). Education and Technology: Critical Perspectives. London, UK: Routledge.Shneiderman B. (2022). Human-Centered AI. Oxford, UK: Oxford University Press.Şimşek A. S., Cengiz G. Ş. T., Bal M. (2025). Extending the TAM framework: exploring learning motivation and agility in educational adoption of generative AI. Educ. Inf. Technol. 30:20913. doi: 10.1007/s10639-025-13591-910.1007/s10639-025-13591-9Sourani M. (2019). Artificial intelligence: a prospective or real option for education. Al Jinan 23:11.Sutedjo A., Liu S. P., Chowdhury M. (2025). Generative AI in higher education: a cross-institutional study on faculty preparation and resources. Stud. Technol. Enhanc. Learn. 4, 1–17. doi: 10.21428/8c225f6e.955a547e10.21428/8c225f6e.955a547eTao Y., Viberg O., Baker R. S., Kizilcec R. F. (2024). Cultural bias and cultural alignment of large language models. PNAS Nexus 3:346.PMC1140728039290441Țîru L. G., Gherheș V., Stoicov I., Stanici M. (2025). Not ready for AI? Exploring teachers’ negative attitudes toward artificial intelligence. Societies 15:337. doi: 10.3390/soc1512033710.3390/soc15120337Tsao J. (2025). Trajectories of AI policy in higher education: interpretations, discourses, and enactments of students and teachers. Comput. Educ. Artif. Intell. 9:100496. doi: 10.1016/j.caeai.2025.10049610.1016/j.caeai.2025.100496Türk N., Batuk B., Kaya A., Yıldırım O. (2025). What makes university students accept generative artificial intelligence? A moderated mediation model. BMC Psychol. 13:1257. doi: 10.1186/s40359-025-03559-2,10.1186/s40359-025-03559-2PMC1261371341225658Uludağ F., Kılıç E., Çelik H. E. (2025). Artificial intelligence, social influence, and AI anxiety: analyzing the intentions of science doctoral students to use ChatGPT with PLS-SEM. Humanit. Soc. Sci. Commun. 12:1308. doi: 10.1057/s41599-025-05641-x10.1057/s41599-025-05641-xVartiainen H., Valtonen T., Kahila J., Tedre M. (2025). ChatGPT and imaginaries of the future of education: insights of Finnish teacher educators. Inf. Learn. Sci. 126, 75–90.Vorobyeva K. I., Belous S., Savchenko N. V., Smirnova L. M., Nikitina S. A., Zhdanov S. P. (2025). Personalized learning through AI: pedagogical approaches and critical insights. Contemp. Educ. Technol. 17:ep574. doi: 10.30935/cedtech/1610810.30935/cedtech/16108Wang S., Wang F., Zhu Z., Wang J., Tran T., Du Z. (2024). Artificial intelligence in education: a systematic literature review. Expert Syst. Appl. 252:124167. doi: 10.1016/j.eswa.2024.12416710.1016/j.eswa.2024.124167Xia L., Shen K., Sun H., An X., Dong Y. (2025). Developing and validating the student learning agency scale in generative artificial intelligence (AI)-supported contexts. Educ. Inf. Technol. 30, 13999–14021.Zhao J., Chapman E., Sabet P. G. (2024). Generative AI and educational assessments: a systematic review. Educ. Res. Perspect. 51, 124–155. doi: 10.70953/erpv51.241200610.70953/erpv51.2412006Zhou X., Teng D., Al-Samarraie H. (2024). The mediating role of generative AI self-regulation on students’ critical thinking and problem-solving. Educ. Sci. 14:1302. doi: 10.3390/educsci1412130210.3390/educsci14121302Zimmerman B. J. (2000). “Attaining self-regulation: a social cognitive perspective,” in Handbook of Self-Regulation, eds. Boekaerts M., Pintrich P. R., Zeidner M. (San Diego, CA: Academic Press; ), 13–39.4194860320260408202604081179-7258172026Advances in medical education and practiceAdv Med Educ PractScholarly Research Output and Trends in Medical Education from the Arab Region (2005-2024): A Bibliometric Analysis.58293158293158293110.2147/AMEP.S582931Medical education research (MER) is crucial for aligning educational strategies with healthcare demands. Despite growing interest in the Arab region, little is known about its regional research productivity, collaboration patterns, and thematic evolution. This study aimed to assess the performance and trends of research in the field of medical education from the Arab region.A bibliometric analysis was conducted using the Web of Science (WoS) database, covering publications on medical education from 2005 to 2024 across 22 Arab countries. Bibliometric indicators, at the document, author, and country levels, along with collaboration network and thematic trends, were analyzed using the R-bibliometrix package.Out of 47,237 global articles, the Arab region contributed 1844 (3.9%), led by Saudi Arabia (n=902), the UAE, and Egypt. A notable increase in publication volume was observed from 2019 onward. The Egyptian Knowledge Bank and King Saud University emerged as leading affiliations and funders. BMC Medical Education and Medical Teacher were the most productive journals. The analysis identified variations in publication volume and citation impact across the Arab region, with highly cited publications largely focusing on the development of regional medical education frameworks and the expanding use of e-learning approaches. The thematic evolution of medical education research was observed across 3 distinct phases: a foundational phase (2005-2010), a developmental phase (2011-2019), and a consolidation and innovation phase (2022-2024). Saudi Arabia showed the highest national productivity, while Qatar and the UAE demonstrated strong international collaboration.Medical education research in the Arab world has grown in volume and complexity over the last two decades, with diverse contributions and Saudi Arabia leading the regional output. Future efforts should focus on sustaining this momentum by addressing the underrepresentation of certain countries and by strengthening both regional and international collaborations.© 2026 Imran et al.ImranMuhammadM0000-0003-1262-9001Department of Surgery, Faculty of Medicine, King Abdulaziz University, Rabigh, Saudi Arabia.Medical Education Unit, Faculty of Medicine, King Abdulaziz University, Rabigh, Saudi Arabia.MalikAhmad AzamAA0000-0001-5051-0058Department of Family and Community Medicine, Faculty of Medicine, King Abdulaziz University, Rabigh, Saudi Arabia.ZubairiNadeem AlamNA0000-0002-0343-0052Department of Pediatrics, Faculty of Medicine, King Abdulaziz University, Rabigh, Saudi Arabia.AnwerFahadF0000-0001-7924-5139Department of Family and Community Medicine, Faculty of Medicine, King Abdulaziz University, Rabigh, Saudi Arabia.BashirMuhammad AbidMA0000-0002-0071-0876Department of Surgery, Faculty of Medicine, King Abdulaziz University, Rabigh, Saudi Arabia.engJournal Article20260402New ZealandAdv Med Educ Pract1015627001179-7258Arab countriesbibliometricsmedical education researchresearch productivityweb of scienceMuhammad Imran and Ahmad Azam Malik contributed equally as first authors. The authors declare that they have no competing interests.2025111920263720264812172026481216202648432202642epublish41948603PMC1305225310.2147/AMEP.S582931582931Taylor DC, Hamdy H. Adult learning theories: implications for learning and teaching in medical education: AMEE Guide No. 83. Med Teach. 2013;35(11):e1561–14. doi: 10.3109/0142159X.2013.828153
10.3109/0142159X.2013.82815324004029Harden RM. The future of health professions education. Med Teach. 2024;46(4):436–437. doi: 10.1080/0142159X.2024.2320521
10.1080/0142159X.2024.232052138430245Harden RM, Grant J, Buckley G, et al. BEME guide no. 1: best evidence medical education. Med Teach. 1999;21(6):553–562. doi: 10.1080/01421599978960
10.1080/0142159997896021281174Cook DA, Bordage G, Schmidt HG. Description, justification and clarification: a framework for classifying the purposes of research in medical education. Med Educ. 2008;42(2):128–133. doi: 10.1111/j.1365-2923.2007.02974.x
10.1111/j.1365-2923.2007.02974.x18194162Majumder MAA, Haque M, Razzaque MS. Trends and challenges of medical education in the changing academic and public health environment of the 21st century. Front Commun. 2023;8:1153764. doi: 10.3389/fcomm.2023.115376410.3389/fcomm.2023.1153764Doja A, Horsley T, Sampson M. Productivity in medical education research: an examination of countries of origin. BMC Med Educ. 2014;14:243. doi: 10.1186/s12909-014-0243-8
10.1186/s12909-014-0243-8PMC423931625404502Peterson WJ, Santen SA, House JB, et al. Increasing education research productivity: a network analysis. West J Emerg Med. 2019;21(1):163–168. doi: 10.5811/westjem.2019.12.44512
10.5811/westjem.2019.12.44512PMC694869631913839Borowczyk M, Stalmach-Przygoda A, Doroszewska A, et al. Developing an effective and comprehensive communication curriculum for undergraduate medical education in Poland–the review and recommendations. BMC Med Educ. 2023;23(1):645. doi: 10.1186/s12909-023-04533-5
10.1186/s12909-023-04533-5PMC1048609337679670Bowe CM, Armstrong E. Assessment for systems learning: a holistic assessment framework to support decision making across the medical education continuum. Acad Med. 2017;92(5):585–592. doi: 10.1097/ACM.0000000000001321
10.1097/ACM.000000000000132127465232Jamaludin TSS, Nurumal M, Ahmad N, et al. Soft skills elements in structured clinical skill assessment: a qualitative study. Bali Med J. 2022;11(3):1666–1674. doi: 10.15562/bmj.v11i3.372110.15562/bmj.v11i3.3721Castro R. Blended learning in higher education: trends and capabilities. Educ Inf Technol. 2019;24(4):2523–2546. doi: 10.1007/s10639-019-09886-310.1007/s10639-019-09886-3Hassell LA, Absar SF, Chauhan C, et al. Pathology education powered by virtual and digital transformation: now and the future. Arch Pathol Lab Med. 2023;147(4):474–491. doi: 10.5858/arpa.2021-0473-RA
10.5858/arpa.2021-0473-RA35878400Wijnen-Meijer M. Implications of internationalisation of medical education. BMC Med Educ. 2023;23(1):640. doi: 10.1186/s12909-023-04630-5
10.1186/s12909-023-04630-5PMC1048611537679717Findyartini A, Nirula L, Mandache M-E, et al. From criteria to impact: the ASPIRE framework as a roadmap for faculty development excellence in health professions education. Med Teach. 2025;47:1–13.
40411806Meo SA, Hassan A, Aqil M, et al. Medical education research in GCC countries. BMC Med Educ. 2015;15:8. doi: 10.1186/s12909-015-0293-6
10.1186/s12909-015-0293-6PMC433098425638308Abdalla S, Ramadan E, Al-Belushi MAK, et al. Unveiling the role of Arab universities in advancing sustainable development goals: a multi-dimensional analysis. Sustainability. 2024;16(14):5829. doi: 10.3390/su1614582910.3390/su16145829Mohiuddin K, Nasr OA, Nadhmi Miladi M, et al. Potentialities and priorities for higher educational development in Saudi Arabia for the next decade: critical reflections of the vision 2030 framework. Heliyon. 2023;9(5):e16368. doi: 10.1016/j.heliyon.2023.e16368
10.1016/j.heliyon.2023.e16368PMC1022024037251831Almuhaidib S, Alqahtani R, Alotaibi HF, et al. Mapping the landscape of medical research in the Arab world countries: a comprehensive bibliometric analysis. Saudi Med J. 2024;45(4):387–396. doi: 10.15537/smj.2024.45.4.20230968
10.15537/smj.2024.45.4.20230968PMC1114757938657984Alameri H, Hamdy H, Sims D. Medical education in the United Arab Emirates: challenges and opportunities. Med Teach. 2021;43(6):625–632. doi: 10.1080/0142159X.2021.1908978
10.1080/0142159X.2021.190897833915071Donthu N, Kumar S, Mukherjee D, et al. How to conduct a bibliometric analysis: an overview and guidelines. J Bus Res. 2021;133:285–296. doi: 10.1016/j.jbusres.2021.04.07010.1016/j.jbusres.2021.04.070Kumar M, George RJ, PS A. Bibliometric analysis for medical research. Indian J Psychol Med. 2023;45(3):277–282. doi: 10.1177/02537176221103617
10.1177/02537176221103617PMC1015955637152388Ninkov A, Frank JR, Maggio LA. Bibliometrics: methods for studying academic publishing. Perspect Med Educ. 2022;11(3):173–176. doi: 10.1007/S40037-021-00695-4
10.1007/S40037-021-00695-4PMC924016034914027Malik AA, Baig M, Butt NS, et al. Bibliometric analysis of global research productivity on vitamin D and bone metabolism (2001–2020): learn from the past to plan future. Nutrients. 2022;14(3):542. doi: 10.3390/nu14030542
10.3390/nu14030542PMC883832635276901Pranckutė R. Web of Science (WoS) and Scopus: the titans of bibliographic information in today’s academic world. Publications. 2021;9(1):12. doi: 10.3390/publications901001210.3390/publications9010012Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:1.PMC800592433782057Imran M, Malik AA, Shahzad R, Hussain B, Ahmed R. A scientometric analysis of research productivity in surgery from Arab countries (2001-2021): research productivity in surgery from Arab countries. Pak J Health Sci. 2023;17:21–28. doi: 10.53350/pjmhs20231762110.53350/pjmhs202317621Aria M, Cuccurullo C. bibliometrix: an R-tool for comprehensive science mapping analysis. J Informetr. 2017;11(4):959–975. doi: 10.1016/j.joi.2017.08.00710.1016/j.joi.2017.08.007Zaini RG, Bin Abdulrahman KA, Al-Khotani AA, et al. Saudi Meds: a competence specification for Saudi medical graduates. Med Teach. 2011;33(7):582–584. doi: 10.3109/0142159X.2011.578180
10.3109/0142159X.2011.57818021696288Khalil R, Mansour AE, Fadda WA, et al. The sudden transition to synchronized online learning during the COVID-19 pandemic in Saudi Arabia: a qualitative study exploring medical students’ perspectives. BMC Med Educ. 2020;20(1):285. doi: 10.1186/s12909-020-02208-z
10.1186/s12909-020-02208-zPMC745368632859188Bin Abdulrahman KA. The current status of medical education in the Gulf Cooperation Council countries. Ann Saudi Med. 2008;28(2):83–88. doi: 10.5144/0256-4947.2008.83
10.5144/0256-4947.2008.83PMC607451718398281Alsoufi A, Alsuyihili A, Msherghi A, et al. Impact of the COVID-19 pandemic on medical education: medical students’ knowledge, attitudes, and practices regarding electronic learning. PLoS One. 2020;15(11):e0242905. doi: 10.1371/journal.pone.0242905
10.1371/journal.pone.0242905PMC768812433237962Al-Balas M, Al-Balas HI, Jaber HM, et al. Distance learning in clinical medical education amid COVID-19 pandemic in Jordan: current situation, challenges, and perspectives. BMC Med Edu. 2020;20(1):341. doi: 10.1186/s12909-020-02257-410.1186/s12909-020-02257-4PMC753087933008392Rajab MH, Gazal AM, Alkattan K. Challenges to online medical education during the COVID-19 pandemic. Cureus J Med Sci. 2020;12(7):1.PMC739872432766008Abdulghani HM, AlKanhal AA, Mahmoud ES, et al. Stress and its effects on medical students: a cross-sectional study at a college of medicine in Saudi Arabia. J Health Popul Nutr. 2011;29(5):516. doi: 10.3329/jhpn.v29i5.8906
10.3329/jhpn.v29i5.8906PMC322511422106758Ibrahim H, Al Tatari H, Holmboe ES. The transition to competency-based pediatric training in the United Arab Emirates. BMC Med Educ. 2015;15(1):65. doi: 10.1186/s12909-015-0340-3
10.1186/s12909-015-0340-3PMC439007925889202Fahal AH. Medical education in the Sudan: its strengths and weaknesses. Med Teach. 2007;29(9–10):910–914. doi: 10.1080/01421590701812991
10.1080/0142159070181299118158664Thalib HI, Zobairi A, Javed Z, et al. Enhancing healthcare research in Saudi Arabia according to vision 2030: an in-depth review. J Res Med Dent Sci. 2024;12:01–08.Mohsen MA, Ho Y-S. Thirty years of educational research in Saudi Arabia: a bibliometric study. Interact Learn Environ. 2024;32(5):1763–1778.Al Marzouqi AHH, Alameddine M, Sharif A, et al. Research productivity in the United Arab Emirates: a 20-year bibliometric analysis. Heliyon. 2019;5(12):1.PMC691195431872101Sywelem MMG. Challenges of international ranking of Egyptian universities from the Academicians’ perspective. Am J Educ Res. 2020;8(7):465–474. doi: 10.12691/education-8-7-310.12691/education-8-7-3Oluwadele D, Singh Y, Adeliyi TT. Trends and insights in e-learning in medical education: a bibliometric analysis. Rev Educ. 2023;11(3):e3431. doi: 10.1002/rev3.343110.1002/rev3.3431PMC985869136673600Gaur U, Majumder MAA, Sa B, et al. Challenges and opportunities of preclinical medical education: COVID-19 crisis and beyond. SN Compr Clin Med. 2020;2(11):1992–1997. doi: 10.1007/s42399-020-00528-1
10.1007/s42399-020-00528-1PMC750842232984766Tekian A, Harris I. Preparing health professions education leaders worldwide: a description of masters-level programs. Med Teach. 2012;34(1):52–58. doi: 10.3109/0142159X.2011.599895
10.3109/0142159X.2011.59989522250675O’Callaghan C, Sandars J, Brown J, et al. The value of master’s degree programmes in health professions education: a scoping review. Clin Teach. 2024;21(4):e13758. doi: 10.1111/tct.13758
10.1111/tct.1375838643984Tekian A, Dekhtyar M, Park YS. The rapid growth and expansion of Master of health professions education (MHPE) programs: a mixed-methods study of international curricular trends and guidelines for programs. Med Teach. 2024;46(3):341–348. doi: 10.1080/0142159X.2023.2284657
10.1080/0142159X.2023.228465737988755El Rassi R, Meho LI, Nahlawi A, et al. Medical research productivity in the Arab countries: 2007-2016 bibliometric analysis. J Glob Health. 2018;8(2):020411. doi: 10.7189/jogh.08.020411
10.7189/jogh.08.020411PMC622035330410737Ul Haq I, Ur Rehman S, Al-Kadri HM, et al. Research productivity in the health sciences in Saudi Arabia: 2008-2017. Ann Saudi Med. 2020;40(2):147–154. doi: 10.5144/0256-4947.2020.147
10.5144/0256-4947.2020.147PMC711823632241169AbdelKader AF, Sayed MH. Evaluation of the Egyptian knowledge bank using the information systems success model. J Acad Librariansh. 2022;48(2):102506. doi: 10.1016/j.acalib.2022.10250610.1016/j.acalib.2022.102506PMC886351935221394Babour A, Alzamzami O, Adedayo A, et al. International publication trends and collaboration impact on the scientific research of Saudi Arabia. J Sciento Res. 2021;10(3):328–336. doi: 10.5530/jscires.10.3.4910.5530/jscires.10.3.49Albert M, Rowland P, Friesen F, et al. Interdisciplinarity in medical education research: myth and reality. Adv Health Sci Educ Theory Pract. 2020;25(5):1243–1253. doi: 10.1007/s10459-020-09977-8
10.1007/s10459-020-09977-8PMC770450732583329Al-Zalabani A, Habib F. Journal of Taibah University Medical Sciences: 16 years of success, and the journey continues. J Taibah Univ Med Sci. 2022;17(2):163–164. doi: 10.1016/j.jtumed.2022.01.001
10.1016/j.jtumed.2022.01.001PMC907387735592797Kim JW, Myung SJ, Yoon HB, et al. How medical education survives and evolves during COVID-19: our experience and future direction. PLoS One. 2020;15(12):e0243958. doi: 10.1371/journal.pone.0243958
10.1371/journal.pone.0243958PMC774828333338045Li R, Wu T. Evolution of artificial intelligence in medical education from 2000 to 2024: bibliometric analysis. Interact J Med Res. 2025;14(1):e63775. doi: 10.2196/63775
10.2196/63775PMC118269363988392641942840202604061097-00102026Apr06Journal of the science of food and agricultureJ Sci Food AgricGenomics-assisted pyramiding of sugary1 and shrunken2 genes for enhancement of kernel sucrose in sweetcorn hybrids.10.1002/jsfa.70617Sweetcorn, owing to its kernel sweetness, has emerged as a crop of choice for its diverse usage worldwide. A large number of sweetcorn cultivars with a recessive shrunken2 (sh2) or sugary1 (su1) genes are available globally. However, a limited number of double-recessive (su1su1sh2sh2) sweetcorn hybrids with enhanced kernel sweetness are available.We introgressed su1 into the parental inbreds of two widely cultivated sh2-based sweetcorn hybrids - PSSC-1 (PMI-SWT019 × PMI-SWT020) and PSSC-2 (PMI-SWT016 × PMI-SWT017), using genomics-assisted breeding. Foreground selection was successfully undertaken in BC1F1, BC2F1 and BC2F2 generations using SuDel36 and SNP_AG1 markers specific to su1 and sh2 genes, respectively. Both genes segregated according to the Mendelian inheritance pattern, with few exceptions. Background selection using 100-114 simple sequence repeats facilitated recovery of >90% of the recurrent parent genome across crosses. Introgressed inbreds (su1su1sh2sh2) possessed 438 g kg-1 sucrose and 74 g kg-1 phytoglycogen over 261 and 52 g kg-1, respectively, among original versions. Reconstituted sweetcorn hybrids also possessed significantly higher sucrose (439 g kg-1) and phytoglycogen (77 g kg-1) over original hybrids (sucrose: 255 g kg-1; phytoglycogen: 55 g kg-1), amounting to 72% and 40% enhancement, respectively. The dehusked cob yield of new sweetcorn hybrids were at par with their original versions. Improved sweetcorn hybrids possessed similar agronomic and DUS (distinctness, uniformity and stability) characters comparable to the original versions.These su1su1sh2sh2 hybrids offer significant commercial value for fresh consumption, catering and processing markets to meet evolving consumer preferences for sweeter, high-quality sweetcorn, as their higher sugar levels enhance taste and consumer appeal, thereby increasing market demand and income opportunities for farmers. © 2026 Society of Chemical Industry.© 2026 Society of Chemical Industry.ChhabraRashmiRICAR - Indian Agricultural Research Institute, New Delhi, India.MuthusamyVigneshV0000-0003-3169-890XICAR - Indian Agricultural Research Institute, New Delhi, India.MishraSubhra JSJICAR - Indian Agricultural Research Institute, New Delhi, India.GulIrumIICAR - Indian Agricultural Research Institute, New Delhi, India.DuoHriipulouHICAR - Indian Agricultural Research Institute, New Delhi, India.GainNisritaNICAR - Indian Agricultural Research Institute, New Delhi, India.ChandGulabGICAR - Indian Agricultural Research Institute, New Delhi, India.KasanaRavindra KRKICAR - Indian Agricultural Research Institute, New Delhi, India.ZunjareRajkumar URU0000-0001-5001-2681ICAR - Indian Agricultural Research Institute, New Delhi, India.HossainFirozF0000-0001-6662-7752ICAR - Indian Agricultural Research Institute, New Delhi, India.engWOS-A/LS-306/2018DEPARTMENT OF SCIENCE AND TECHNOLOGY, Ministry of Science and Technology, GOVT. OF INDIAJournal Article20260406EnglandJ Sci Food Agric03763340022-5142IMdouble‐recessive sweetcornshrunken2sugary1sweetness20262262025112820263112026470312026470312026462332aheadofprint4194284010.1002/jsfa.70617REFERENCESChhabra R, Zunjare RU, Gul I, Mishra SJ, Duo H, Bhatt V et al., Enrichment of phytoglycogen in sweet corn kernels through genomics‐assisted selection. Funct Integr Genomics 25:212 (2025).Hu Y, Colantonio V, Müller BS, Leach KA, Nanni A, Finegan C et al., Genome assembly and population genomic analysis provide insights into the evolution of modern sweet corn. Nat Commun 12:1227 (2021).Gan Y, Xiao G, Fang W, Xie R, Zhang L and Liu X, A comparative study of sweet corn industry development between the United States and China. IOSR J Agric Vet Sci 18:41–48 (2025).FAOSTAT, Accessed on 11‐02‐2025 (2023).Swapna G, Jadesha G and Mahadevu P, Sweet corn–a future healthy human nutrition food. Int J Curr Microbiol App Sci 9:3859–3865 (2020).Tracy WF, Sweet corn, in specialty corns. CRC Press, Boca Raton, USA, pp. 167–210 (2000).Boyer CD and Shannon JC, The use of endosperm genes for sweet corn improvement. Plant Breed Rev 1:139–161 (1983).Creech RG, Genetic control of carbohydrate synthesis in maize endosperm. Genetics 52:1175–1186 (1965).Hossain F, Nepolean T, Vishwakarma AK, Pandey N, Prasanna BM and Gupta HS, Mapping and validation of microsatellite markers linked to sugary1 and shrunken2 genes in maize (Zea mays L.). J Plant Biochem Biotechnol 24:135–142 (2015).Tsai CY and Glover DV, Effect of the brittle‐1 sugary‐1 double mutant combination on carbohydrate and postharvest quality of sweet corn 1. Crop Sci 14:808–810 (1974).Panatee P, Dermail A, Chankaew S, Lertrat K, Simla S, Lomthaisong K et al., Seed germination and composition of double and triple endosperm mutants of tropical sweet corn under different harvest and storage conditions. HortTechnology 34:789–800 (2024).Jie GO, Leilei SU, Liping ZH, Qingfeng WA, Ruichun YA, Xiaoqin LI et al., Creation and evaluation for corn germplasm of double recessive sweet‐waxy genotype. J South China Agric Univ 40:6–13 (2023).Creech RG, Carbohydrate synthesis in maize. Adv Agron 20:275–322 (1968).Hossain F, Jaiswal SK, Muthusamy V, Zunjare RU, Mishra SJ et al., Enhancement of nutritional quality in maize (Zea mays L.) kernel through marker‐assisted breeding for vte4, crtRB1, and opaque2 genes. J Appl Genet 64:431–443 (2023). https://doi.org/10.1007/s13353-023-00768-6.Chhabra R, Hossain F, Muthusamy V, Baveja A, Mehta BK and Zunjare RU, Development and validation of breeder‐friendly functional markers of sugary1 gene encoding starch‐debranching enzyme affecting kernel sweetness in maize (Zea mays). Crop Pasture Sci 70:868–875 (2019a).Chhabra R, Hossain F, Muthusamy V, Baveja A, Mehta BK and Uttamrao Zunjare R, Development and validation of gene‐based markers for shrunken2‐reference allele and their utilization in marker‐assisted sweet corn (Zea mays Sachharata) breeding programme. Plant Breed 139:1135–1144 (2020).Hossain F, Muthusamy V, Pandey N, Vishwakarma AK, Baveja A et al., Marker‐assisted introgression of opaque2 allele for rapid conversion of elite hybrids into quality protein maize. J Genet 97:571–583 (2018). https://doi.org/10.1007/s12041-018-0914-z.Saghai‐Maroof MA, Soliman KM, Jorgensen RA and Allard R, Ribosomal DNA spacer‐length polymorphisms in barley: mendelian inheritance, chromosomal location, and population dynamics. Proc Natl Acad Sci U S A 81:8014–8018 (1984).Muthusamy V, Hossain F, Thirunavukkarasu N, Saha S, Agrawal PK and Gupta HS, Genetic analyses of kernel carotenoids in novel maize genotypes possessing rare allele of β‐carotene hydroxylase gene. Cereal Res Commun 44:669–680 (2016).Baveja A, Muthusamy V, Panda KK, Zunjare RU, Das AK, Chhabra R et al., Development of multinutrient‐rich biofortified sweet corn hybrids through genomics‐assisted selection of shrunken2, opaque2, lcyE and crtRB1 genes. J Appl Genet 62:419–429 (2021).PPVFRA, Guidelines for the conduct of test for distinctiveness, uniformity and stability on maize (Zea mays L.) (2007). https://www.plantauthority.gov.in/sites/default/files/gmaize.pdf.Simla S, Lertrat K and Suriharn B, Carbohydrate characters of six vegetable waxy corn varieties as affected by harvest time and storage duration. Asian J Plant Sci 9:463–470 (2010).DuBois M, Gilles KA, Hamilton JK, Rebers PA and Smith F, Colorimetric method for determination of sugars and related substances. Anal Chem 28:350–356 (1956).Benchimol LL, Souza CL Jr and Souza AP, Microsatellite‐assisted backcross selection in maize. Genet Mol Biol 28:789–797 (2005).Van Berloo R, GGT 2.0: versatile software for visualization and analysis of genetic data. J Hered 99:232–236 (2008).Seabold S and Perktold J, Statsmodels: econometric and statistical modeling with python. SciPy 7:92–96 (2010).Steichen T, CHIPLOT: Stata Module to Produce Graphical Assessment Plot for Dependence. Statistical Software Components S420301, Boston College Department of Economics, revised 23 Sep 2001.Mushtaq A, Yousouf M, Malik MA, Mukhtar T, Amin T, Showkat S et al., Post‐harvest quality management of sweet corn: disorders, losses and preservation strategies. Food Nutr 1:100007 (2025).Ashraf H, Wu Y, Xiao Y, Liang X, Li X, Shokat S et al., Integrating GWAS and gene expression to decipher genetic components of pericarp thickness and soluble sugars in sweet corn. BMC Plant Biol 26:293 (2026). https://doi.org/10.1186/s12870-026-08165-x.Tas T and Mutlu A, Morpho‐physiological effects of environmental stress on yield and quality of sweet corn varieties (Zea mays L.). Peer J 9:e12613 (2021).Chhabra R, Muthusamy V, Gain N, Katral A, Prakash NR, Zunjare RU et al., Allelic variation in sugary1 gene affecting kernel sweetness among diverse‐mutant and‐wild‐type maize inbreds. Mol Genet Genomics 296:1085–1102 (2021).Chhabra R, Muthusamy V, Baveja A, Katral A, Mehta B, Zunjare RU et al., Allelic variation in shrunken2 gene affecting kernel sweetness in exotic‐and indigenous‐maize inbreds. PLoS One 17:e0274732 (2022).Chen B, Feng S, Hou J, Zhu Y, Bao F, Han H et al., Genome‐wide transcriptome analysis revealing the genes related to sugar metabolism in kernels of sweet corn. Metabolites 12:1254 (2022).Hershberger J, Tanaka R, Wood JC, Kaczmar N, Wu D, Hamilton JP et al., Transcriptome‐wide association and prediction for carotenoids and tocochromanols in fresh sweet corn kernels. Plant Genome 15:e20197 (2022).Ruanjaichon V, Khammona K, Thunnom B, Suriharn K, Kerdsri C, Aesomnuk W et al., Identification of gene associated with sweetness in corn (Zea mays L.) by genome‐wide association study (GWAS) and development of a functional SNP marker for predicting sweet corn. Plants 10:1239 (2021).Tracy WF, Shuler SL and Dodson‐Swenson H, The use of endosperm genes for sweet corn improvement: a review of developments in endosperm genes in sweet corn since the seminal publication in plant breeding reviews, volume 1, by Charles Boyer and Jack Shannon (1984). Plant Breed Rev 43:215–241 (2019).Allam M, Ordás B, Djemel A, Tracy WF and Revilla P, Linkage disequilibrium between fitness QTLs and the sugary1 allele of maize. Mol Breed 39:3 (2019).Neuffer MG, Coe EH and Wessler SR, Mutants of Maize. Cold Spring Harbor Laboratory Press, New York, p. 468 (1997).Zanders SE and Unckless RL, Fertility costs of meiotic drivers. Curr Biol 29:R512–R520 (2019).Mahato A, Shahi JP, Singh PK and Kumar M, Genetic diversity of sweet corn inbreds using agro‐morphological traits and microsatellite markers. 3 Biotech 8:332 (2018).Bonucci PA, Production of hybrid “improved supersweet” sweet corn. US Patent Number 4:630–693 (1986).Mangelsdorf PC, Corn: its origin, evolution and improvement, in Corn. Harvard University Press, Cambridge, Massachusetts, USA (2013).Tracy WF, Sweet corn: Zea mays L., in Genetic Improvement of Vegetable Crops. Pergamon Press, Oxford, United Kingdom, pp. 777–807 (1993).Talukder ZA, Muthusamy V, Chhabra R, Bhatt V, Reddappa SB, Mishra SJ et al., Enrichment of amylopectin in sub‐tropically adapted maize hybrids through genomics‐assisted introgression of waxy1 gene encoding granule‐bound starch synthase (GBSS). J Cereal Sci 105:103443 (2022).Chauhan HS, Zunjare RU, Rashmi T, Muthusamy V, Das AK, Mishra SJ et al., Enrichment of vitamin a and vitamin E in sweet corn kernels through genomics‐assisted introgression of mutant version of crtRB1 and vte4 genes. Appl Biochem Biotechnol 197:1889–1905 (2025).Civardi L, Xia Y, Edwards KJ, Schnable PS and Nikolau BJ, The relationship between genetic and physical distances in the cloned a1‐sh2 interval of the Zea mays L. genome. Proc Natl Acad Sci 91:8268–8272 (1994).Chhabra R, Hossain F, Muthusamy V, Baveja A, Mehta B and Zunjare RU, Mapping and validation of Anthocyanin1 pigmentation gene for its effectiveness in early selection of shrunken2 gene governing kernel sweetness in maize. J Cereal Sci 87:258–265 (2019b).Sharma G, Chhabra R, Muthusamy V, Zunjare RU, Sarma GR, Sapkal DR et al., Unraveling molecular architecture, development and validation of Anthocyanin1 gene‐based novel breeder‐friendly markers for utilization in genomics‐assisted breeding of blue maize. South Afr J Bot 172:267–276 (2024).Reddappa SB, Chhabra R, Muthusamy V, Zunjare RU, Talukder ZA, Mishra SJ et al., Characterization of sub‐tropically adapted maize breeding lines for loci governing kernel amylose and resistant starch. Starch 76:2300289 (2024).Choudhary M, Muthusamy V, Hossain F, Thirunavukkarasu N, Pandey N, Jha SK et al., Characterization of β‐carotene rich MAS‐derived maize inbreds possessing rare genetic variation in β ‐carotene hydroxylase gene. Indian J Genet Plant Breed 74:620–623 (2014).Giroux MJ and Hannah LC, ADP‐glucose pyrophosphorylase in shrunken‐2 and brittle‐2 mutants of maize. Mol Gen Genet 243:400–408 (1994).Demeter C, Nagy J, Huzsvai L, Zelenák A, Szabó A and Széles A, Analysis of the content values of sweet maize (Zea mays L. Convar Saccharata Koern) in precision farming. Agron 11:2596 (2021).Soberalske RM and Andrew RH, Gene effects on kernel moisture and sugars of near‐isogenic lines of sweet corn. Crop Sci 18:743–746 (1978).Lehner B, Molecular mechanisms of epistasis within and between genes. Trends Genet 27:323–331 (2011).Creech R and McArdle FJ, Gene interaction for quantitative changes in carbohydrates in maize kernels. Crop Sci 6:192–194 (1966).Rimpler A, Kiers HA and van Ravenzwaaij D, To interact or not to interact: the pros and cons of including interactions in linear regression models. Behav Res Methods 57:92 (2025).Dodson‐Swenson HG and Tracy WF, Endosperm carbohydrate composition and kernel characteristics of shrunken2‐intermediate (sh2‐i/sh2‐i Su1/Su1) and shrunken2‐intermediate–sugary1‐reference (sh2‐i/sh2‐i su1‐r/su1‐r) in sweet corn. Crop Sci 55:2647–2656 (2015).Mehta BK, Hossain F, Muthusamy V, Zunjare RU, Sekhar JC and Gupta HS, Analyzing the role of sowing and harvest time as factors for selecting super sweet (sh2sh2) corn hybrids. Indian J Genet Plant Breed 77:348–356 (2017).4193949020260406202604062046-1402142025F1000ResearchF1000ResAnalysis of research on gamification, critical thinking and academic performance: A systematic review in the context of higher education.91391310.12688/f1000research.164232.2Gamification has become relevant in the field of higher education, facilitating the teaching and learning process. There are studies that show a positive relationship between gamification and academic performance. A systematic literature review was developed, applying the prism method and using three databases: Scopus, WOS and Scielo. The results indicate a notorious orientation of the studies towards quantitative research. Ninety-two percent of the selected documents have students as their population, while 6% are oriented to teachers and 2% to both. In the more in-depth results, it can be demonstrated that gamification is linked to the generation of critical thinking and the improvement of academic performance in the context of higher education. The analysis of theoretical structures reveals that the most relevant approaches for the study of gamification are self-determination and gamified learning. It is also evident that there is a positive relationship between gamification and motivation in learning processes, which allows encouraging the development of critical thinking and the improvement of academic performance.Copyright: © 2026 Gamarra-Vargas NM et al.Gamarra-VargasNatalie MargaritaNM0000-0002-7289-6462Graduate School, Universidad Peruana de Ciencias Aplicadas, Lima District, Lima Region, 15076, Peru.Cosio BordaRicardo FernandoRF0009-0005-7259-608XFaculty of Administration, Finance and Economic Sciences, Universidad EAN, Bogotá, Bogota, 111321, Colombia.Quispe-FarfánPercy HugoPH0000-0002-8921-486XGraduate School, Universidad Peruana de Ciencias Aplicadas, Lima District, Lima Region, 15076, Peru.Bosmans FloresFabiola GabrielaFG0000-0002-2454-4227Business Faculty, Universidad Peruana de Ciencias Aplicadas, Lima District, Lima Region, 15023, Peru.Robladillo-BravoLiz MaribelLMUniversidad Autónoma de Ica, Chincha Alta, Ica, 11701, Peru.engJournal ArticleSystematic ReviewReview20260225EnglandF1000Res1015943202046-1402IMHumansThinkingAcademic PerformanceLearningUniversitiesacademic performancecritical thinkinggamificationhigher educationNo competing interests were disclosed.202621920264612372026461236202646618epublish41939490PMC1304736910.12688/f1000research.164232.24193915820260406202604060971-62035022025Apr-JunJournal of medical physicsJ Med PhysClinical Applicability of Various Digital Softwares Used in Forensic Odontology for Age Estimation Using Teeth on Cone-beam Computed Tomography Imaging: A Systematic Review and Meta-Analysis.398406398-40610.4103/jmp.jmp_62_25Forensic odontology plays a crucial role in age estimation and personal identification, especially in cases where only dental remains are available. In a similar context, various newer innovations in digital software are a key tool to assess age from such remains in contemporary times. The present systematic review and meta-analysis focuses on the use of various digital software used in forensic odontology for age estimation from dental remains in a range of studies that were published between 2006 and 2023.Relevant studies were sourced from Google Scholar, Directory of Open Access Journals, PubMed, U.S. National Institutes of Health/National Library of Medicine (NIH/NLM), Scimago Journal Ranking, SCOPUS, and Web of Science (WoS) using keywords such as forensic odontology, age estimation, and digital cone-beam computed tomography (CBCT), while only original research articles published between 2006 and 2023 were included. Again, digital software tools such as Adobe Photoshop CS2, AutoCAD, i-Dixel imaging software, ImageJ, ITK-SNAP 3.8, MIMICS, and OsiriX among others were evaluated for their precision and efficacy. The extracted data were organized in an Excel sheet including categories such as author, year, software used, sample size included, age estimation method used, age group included, teeth used, and location of the study, while the data obtained were subjected to statistical analysis.The analysis revealed AutoCAD software to be the most common digital software used with a valid percentage of 31.5 and cumulative percentage of 48.2, followed by MIMICS and Adobe Photoshop CS2 to be the next in order. Furthermore, i-Dixel imaging software was the least common tool used in the list. Again, Kvaal's and Demirjian's methods were the most common methods employed, while pulp/tooth area ratio and pulp/tooth volume were the most common techniques used across the various studies published for age estimation in the defined period in descending order.Advancements in software tools and digital CBCT imaging have significantly improved the precision of age estimation in the field of forensic odontology. These tools possess enhanced features of image segmentation and three-dimensional reconstructions, leading to more accurate and reproducible results. Further research is, however, needed to standardize methods, refine population-specific equations, and improve the applicability of these techniques across diverse populations.Copyright: © 2025 Journal of Medical Physics.KartheekGandikotaGDepartment of Oral Pathology and Microbiology, Vishnu Dental College, Vishnupur, Bhimavaram, Andhra Pradesh, India.ReddyG SanthoshGSDepartment of Oral and Maxillofacial Surgery, Malla Reddy Dental College for Women, Hyderabad, Telangana, India.BalaramKolasaniKDepartment of General Dentistry, Aspen Dental, Manitowoc, WI, USA.ParveenSameenaSDepartment of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, Jazan University, Jazan.VeeralakshmiPPConsultant Endodontist, Bengaluru, Karnataka, India.AbobakerSally HassanSHDepartment of Basic Dental Sciences, Faculty of Dentistry, Mansoura University, Mansoura, AD Daqahliyah, Egypt.BabuJ SureshJSDepartment of Preventive Dental Sciences, Division of Periodontology, College of Dentistry, University of Hail, Hail, Saudi Arabia.SwarnalathaCCDepartment of Preventive Dental Sciences, Division of Periodontology, College of Dentistry, University of Hail, Hail, Saudi Arabia.NayyarAbhishek SinghASDepartment of Oral Medicine and Radiology, Saraswati Dhanwantari Dental College and Hospital and Post-Graduate Research Institute, Parbhani, Maharashtra, India.engJournal Article20250703IndiaJ Med Phys94411040971-6203Age estimationcone-beam computed tomographydental imagingdigital software toolsforensic odontologypulp/tooth area ratioThere are no conflicts of interest.2025382025511202551320264612372026461236202646616202541ppublish41939158PMC1304619410.4103/jmp.jmp_62_25JMP-50-398De Luca S, Alemán I, Bertoldi F, Ferrante L, Mastrangelo P, Cingolani M, et al. Age estimation by tooth/pulp ratio in canines by peri-apical x-rays: Reliability in age determination of Spanish and Italian medieval skeletal remains. J Archaeol Sci. 2010;37:3048–58.Vandevoort FM, Bergmans L, Van Cleynenbreugel J, Bielen DJ, Lambrechts P, Wevers M, et al. Age calculation using X-ray microfocus computed tomographical scanning of teeth: A pilot study. J Forensic Sci. 2004;49:787–90.15317195Soomer H, Ranta H, Lincoln MJ, Penttilä A, Leibur E. Reliability and validity of eight dental age estimation methods for adults. J Forensic Sci. 2003;48:149–52.12570217Rodríguez-Niklitschek C, Oporto GH, Chuhuaicura P, Alemán I, Fonseca GM. Cameriere’s open apices methodology for dental age estimation in children: A scoping review from a Latin American perspective. Forensic Sci Med Pathol. 2024;20:1049–57.37222903Tang N, Antoine D, Hillson S. Application of the Bang and Ramm age at death estimation method to two known-age archaeological assemblages. Am J Phys Anthropol. 2014;155:332–51.25043454Metzger Z, Buchner A, Gorsky M. Gustafson’s method for age determination from teeth – A modification for the use of dentists in identification teams. J Forensic Sci. 1980;25:742–9.7430988Gustafson G. Age determination on teeth. J Am Dent Assoc. 1950;41:45–54.15428197Dalitz GD. Age determination of adult human remains by teeth examination. J Forensic Sci Soc. 1962;3:11–21.Bang G, Ramm E. Determination of age in humans from root dentin transparency. Acta Odontol Scand. 1970;28:3–35.5265990Johanson G. Age determination from teeth. Odontol Rev. 1971;22:1–126.Maples WR. An improved technique using dental histology for estimation of adult age. J Forensic Sci. 1978;23:764–70.744998Solheim T, Sundnes PK. Dental age estimation of Norwegian adults – A comparison of different methods. Forensic Sci Int. 1980;16:7–17.7399381Solheim T. Dental age estimation. An alternative technique for tooth sectioning. Am J Forensic Med Pathol. 1984;5:181–4.6731411Solheim T. Dental root translucency as an indicator of age. Scand J Dent Res. 1989;97:189–97.2740830Solheim T. Amount of secondary dentin as an indicator of age. Scand J Dent Res. 1992;100:193–9.1439521Solheim T, Kvaal S. Dental root surface structure as an indicator of age. J Forensic Odontostomatol. 1993;11:9–21.9227059Solheim T. A new method for dental age estimation in adults. Forensic Sci Int. 1993;59:137–47.8330806Helfman PM, Bada JL. Aspartic acid racemization in tooth enamel from living humans. Proc Natl Acad Sci U S A. 1975;72:2891–4.PMC4328841059082Helfman PM, Bada JL. Aspartic acid racemisation in dentine as a measure of ageing. Nature. 1976;262:279–81.958370Helfman PM, Bada JL, Shou MY. Considerations on the role of aspartic acid racemization in the aging process. Gerontology. 1977;23:419–25.892449Ritz-Timme S, Cattaneo C, Collins MJ, Waite ER, Schütz HW, Kaatsch HJ, et al. Age estimation: The state of the art in relation to the specific demands of forensic practise. Int J Legal Med. 2000;113:129–36.10876982Ritz-Timme S. Aging at the molecular level exemplified by proteins. Z Gerontol Geriatr. 2001;34:452–6.11828883Ritz-Timme S, Collins MJ. Racemization of aspartic acid in human proteins. Ageing Res Rev. 2002;1:43–59.12039448Ritz-Timme S, Laumeier I, Collins M. Age estimation based on aspartic acid racemization in elastin from the yellow ligaments. Int J Legal Med. 2003;117:96–101.12690506Ritz-Timme S, Laumeier I, Collins MJ. Aspartic acid racemization: Evidence for marked longevity of elastin in human skin. Br J Dermatol. 2003;149:951–9.14632798Kvaal S, Solheim T. A non-destructive dental method for age estimation. J Forensic Odontostomatol. 1994;12:6–11.9227083Kvaal SI, Kolltveit KM, Thomsen IO, Solheim T. Age estimation of adults from dental radiographs. Forensic Sci Int. 1995;74:175–85.7557754Cameriere R, Ferrante L, Cingolani M. Variations in pulp/tooth area ratio as an indicator of age: A preliminary study. J Forensic Sci. 2004;49:317–9.15027553Cameriere R, Brogi G, Ferrante L, Mirtella D, Vultaggio C, Cingolani M, et al. Reliability in age determination by pulp/tooth ratio in upper canines in skeletal remains. J Forensic Sci. 2006;51:861–4.16882230Cameriere R, Ferrante L, Cingolani M. Age estimation in children by measurement of open apices in teeth. Int J Legal Med. 2006;120:49–52.16283352Cameriere R, Ferrante L, Belcastro MG, Bonfiglioli B, Rastelli E, Cingolani M. Age estimation by pulp/tooth ratio in canines by peri-apical X-rays. J Forensic Sci. 2007;52:166–70.17209930Cameriere R, Ferrante L, Belcastro MG, Bonfiglioli B, Rastelli E, Cingolani M. Age estimation by pulp/tooth ratio in canines by mesial and vestibular peri-apical X-rays. J Forensic Sci. 2007;52:1151–5.17680998Cameriere R, De Angelis D, Ferrante L, Scarpino F, Cingolani M. Age estimation in children by measurement of open apices in teeth: A European formula. Int J Legal Med. 2007;121:449–53.17549508Cameriere R, Ferrante L, Liversidge HM, Prieto JL, Brkic H. Accuracy of age estimation in children using radiograph of developing teeth. Forensic Sci Int. 2008;176:173–7.17949930Cameriere R, Cunha E, Sassaroli E, Nuzzolese E, Ferrante L. Age estimation by pulp/tooth area ratio in canines: Study of a Portuguese sample to test Cameriere’s method. Forensic Sci Int. 2009;193:128.e1–6.19854595Cameriere R, De Luca S, Alemán I, Ferrante L, Cingolani M. Age estimation by pulp/tooth ratio in lower premolars by orthopantomography. Forensic Sci Int. 2012;214:105–12.21821373Cameriere R, Cunha E, Wasterlain SN, De Luca S, Sassaroli E, Pagliara F, et al. Age estimation by pulp/tooth ratio in lateral and central incisors by peri-apical X-ray. J Forensic Leg Med. 2013;20:530–6.23756528Cameriere R, De Luca S, Egidi N, Bacaloni M, Maponi P, Ferrante L, et al. Automatic age estimation in adults by analysis of canine pulp/tooth ratio: Preliminary results. J Forensic Radiol Imaging. 2015;3:61–6.Cameriere R, Pacifici A, Pacifici L, Polimeni A, Federici F, Cingolani M, et al. Age estimation in children by measurement of open apices in teeth with Bayesian calibration approach. Forensic Sci Int. 2016;258:50–4.26650061Demirjian A, Goldstein H, Tanner JM. A new system of dental age assessment. Hum Biol. 1973;45:211–27.4714564Demirjian A, Goldstein H. New systems for dental maturity based on seven and four teeth. Ann Hum Biol. 1976;3:411–21.984727Demirjian A. Dental development: Index of physiologic maturation. Med Hyg (Geneve) 1978;36:3154–9.372771Roughley M, Liu CY. Digital 2D, 2.5D and 3D methods for adding photo-realistic textures to 3D facial depictions of people from the past. Adv Exp Med Biol. 2022;1356:245–80.35146625Johnson A. Effectiveness of virtual reality and 3D digital tools in forensic dental education and research – A preliminary survey. J Vis Commun Med. 2024;47:62–9.39522162Wilke F, Matthews H, Herrick N, Dopkins N, Claes P, Walsh S. A novel approach to craniofacial analysis using automated 3D landmarking of the skull. Sci Rep. 2024;14:12381.PMC1113714838811771Tynan P. Unveiling decomposition dynamics: Leveraging 3D models for advanced forensic analysis. Int J Legal Med. 2024;138:895–7.38012290Villa C, Lynnerup N, Jacobsen C. A virtual, 3d multimodal approach to victim and crime scene reconstruction. Diagnostics (Basel) 2023;13:2764.PMC1048668037685302Samatas GG, Papakostas GA. Biometrics: Going 3D. Sensors (Basel) 2022;22:6364.PMC946034136080821Carew RM, French J, Morgan RM. 3D forensic science: A new field integrating 3D imaging and 3D printing in crime reconstruction. Forensic Sci Int Synerg. 2021;3:100205.PMC855651034746730Simmons-Ehrhardt T, Falsetti CR, Falsetti AB. Using Computed Tomography (CT) data to build 3d resources for forensic craniofacial identification. Adv Exp Med Biol. 2021;1317:53–74.33945132Roughley MA, Wilkinson CM. The affordances of 3D and 4D digital technologies for computerized facial depiction. Adv Exp Med Biol. 2019;1138:87–101.31313260Katsumura S, Sato K, Ikawa T, Yamamura K, Ando E, Shigeta Y, et al. High-precision, reconstructed 3D model of skull scanned by conebeam CT: Reproducibility verified using CAD/CAM data. Leg Med (Tokyo) 2016;18:37–43.26832374Sakuma A, Ishii M, Yamamoto S, Shimofusa R, Kobayashi K, Motani H, et al. Application of postmortem 3D-CT facial reconstruction for personal identification. J Forensic Sci. 2010;55:1624–9.20707833Liu DX, Wang CL, Liu L, Dong ZY, Ke HF, Yu ZY. The accuracy of 3D-CT volume rendering for craniofacial linear measurements. Shanghai Kou Qiang Yi Xue. 2006;15:517–20.17348228Cavalcanti MG, Rocha SS, Vannier MW. Craniofacial measurements based on 3D-CT volume rendering: Implications for clinical applications. Dentomaxillofac Radiol. 2004;33:170–6.15371317Rocha Sdos S, Ramos DL, Cavalcanti Mde G. Applicability of 3D-CT facial reconstruction for forensic individual identification. Pesqui Odontol Bras. 2003;17:24–8.12908055Olze A, Mahlow A, Schmidt S, Wernecke KD, Geserick G, Schmeling A. Combined determination of selected radiological and morphological variables relevant for dental age estimation of young adults. Homo. 2005;56:133–40.16130836Schmeling A, Reisinger W, Geserick G, Olze A. Age estimation of unaccompanied minors. Part I. General considerations. Forensic Sci Int. 2006;159(Suppl 1):S61–4.16529895Olze A, Reisinger W, Geserick G, Schmeling A. Age estimation of unaccompanied minors. Part II. Dental aspects. Forensic Sci Int. 2006;159(Suppl 1):S65–7.16529893Olze A, Hertel J, Schulz R, Wierer T, Schmeling A. Radiographic evaluation of Gustafson’s criteria for the purpose of forensic age diagnostics. Int J Legal Med. 2012;126:615–21.22580780Navic P, Inthasan C, Chaimongkhol T, Mahakkanukrauh P. Facial reconstruction using 3-D computerized method: A scoping review of methods, current status, and future developments. Leg Med (Tokyo) 2023;62:102239.36940597Guleria A, Krishan K, Sharma V, Kanchan T. Methods of forensic facial reconstruction and human identification: Historical background, significance, and limitations. Naturwissenschaften. 2023;110:8.36807002Wickramasinghe N, Thompson BR, Xiao J. The opportunities and challenges of digital anatomy for medical sciences: Narrative review. JMIR Med Educ. 2022;8:e34687.PMC916665735594064Makino Y, Unuma K, Nolte KB, Adolphi NL. Accuracy of forensic pathologists in incorporating post-mortem CT (PMCT) in forensic death investigation. J Forensic Sci. 2022;67:2351–9.36069005Ebert LC, Franckenberg S, Sieberth T, Schweitzer W, Thali M, Ford J, et al. A review of visualization techniques of post-mortem computed tomography data for forensic death investigations. Int J Legal Med. 2021;135:1855–67.PMC835498233931808Zhao JM, Chu G, Mou QN, Han MQ, Chen T, Hou YX, et al. Research progress and prospect of facial reconstruction in forensic science. Fa Yi Xue Za Zhi. 2020;36:614–21.33295160Ubelaker DH, Wu Y, Cordero QR. Craniofacial photographic superimposition: New developments. Forensic Sci Int Synerg. 2019;1:271–4.PMC721917832411979Cartocci G, Santurro A, Neri M, Zaccagna F, Catalano C, La Russa R, et al. Post-mortem computed tomography (PMCT) radiological findings and assessment in advanced decomposed bodies. Radiol Med. 2019;124:1018–27.31254219Norberti N, Tonelli P, Giaconi C, Nardi C, Focardi M, Nesi G, et al. State of the art in post-mortem computed tomography: A review of current literature. Virchows Arch. 2019;475:139–50.30937612Miranda GE, Wilkinson C, Roughley M, Beaini TL, Melani RF. Assessment of accuracy and recognition of three-dimensional computerized forensic craniofacial reconstruction. PLoS One. 2018;13:e0196770.PMC593163129718983Joseph TI, Girish KL, Sathyan P, Kiran MS, Vidya S. Virtopsy: An integration of forensic science and imageology. J Forensic Dent Sci. 2017;9:111–4.PMC588763129657485Garvin HM, Stock MK. The utility of advanced imaging in forensic anthropology. Acad Forensic Pathol. 2016;6:499–516.PMC647454931239924Short LJ, Khambay B, Ayoub A, Erolin C, Rynn C, Wilkinson C. Validation of a computer modelled forensic facial reconstruction technique using CT data from live subjects: A pilot study. Forensic Sci Int. 2014;237:147.e1–8.24529418Lee WJ, Wilkinson CM, Hwang HS. An accuracy assessment of forensic computerized facial reconstruction employing cone-beam computed tomography from live subjects. J Forensic Sci. 2012;57:318–27.22073932Claes P, Vandermeulen D, De Greef S, Willems G, Clement JG, Suetens P. Computerized craniofacial reconstruction: Conceptual framework and review. Forensic Sci Int. 2010;201:138–45.20359837Pomara C, Fineschi V, Scalzo G, Guglielmi G. Virtopsy versus digital autopsy: Virtual autopsy. Radiol Med. 2009;114:1367–82.19669111Bolliger SA, Thali MJ, Ross S, Buck U, Naether S, Vock P. Virtual autopsy using imaging: Bridging radiologic and forensic sciences. A review of the Virtopsy and similar projects. Eur Radiol. 2008;18:273–82.17705044Lopes PM, Moreira CR, Perrella A, Antunes JL, Cavalcanti MG. 3-D volume rendering maxillofacial analysis of angular measurements by multislice CT. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2008;105:224–30.18230391De Greef S, Willems G. Three-dimensional cranio-facial reconstruction in forensic identification: Latest progress and new tendencies in the 21st century. J Forensic Sci. 2005;50:12–7.15830991De Greef S, Claes P, Mollemans W, Vandermeulen D, Suetens P, Willems G. Computer-assisted facial reconstruction: Recent developments and trends. Rev Belge Med Dent (1984) 2005;60:237–49.16370439Vanezis M, Vanezis P. Cranio-facial reconstruction in forensic identification – Historical development and a review of current practice. Med Sci Law. 2000;40:197–205.10976181Tyrrell AJ, Evison MP, Chamberlain AT, Green MA. Forensic three-dimensional facial reconstruction: Historical review and contemporary developments. J Forensic Sci. 1997;42:653–61.9243827Aulsebrook WA, Işcan MY, Slabbert JH, Becker P. Superimposition and reconstruction in forensic facial identification: A survey. Forensic Sci Int. 1995;75:101–20.85863344193775520260406202604061664-462X172026Frontiers in plant scienceFront Plant SciScientific evolution and translational horizons of plant core germplasm: a global bibliometric synthesis and strategic insights.17711641771164177116410.3389/fpls.2026.1771164The increasing volume of global plant germplasm resources has led to the complexity of plant germplasm resource management. Plant core germplasm collections demonstrate significant potential in improving the efficiency of germplasm resource management, promoting crop improvement, and maintaining biodiversity.This study conducted a comprehensive bibliometric analysis of 2,303 Chinese and English publications (2004-2024) sourced from the Web of Science (WoS) and China National Knowledge Infrastructure (CNKI), offering an integrated bilingual perspective on global research patterns and evolution.Results show sustained growth in the field, with China and the United States as the leading contributors and collaborative hubs. The research trajectory progressed through three distinct phases-foundational, consolidation, and predictive-driven by advances from phenotypic evaluation to high-throughput genomics and genome-wide association studies (GWAS). Keyword evolution reveals a clear paradigm shift from descriptive, phenotype-based management toward allele-driven, predictive breeding platforms. Persistent challenges include data fragmentation, limited sharing, and a strong taxonomic bias toward major cereal crops. Looking forward, we propose the integration of artificial intelligence to establish biodigital resource centers and the development of functionally designed core collections tailored to specific plant groups. These strategies will enhance precision breeding and support sustainable agriculture and global food security.Copyright © 2026 Wang, Ma, Qi, Ye, Lu and Wei.WangWenjunWFaculty of Agronomy, Jilin Agricultural University, Changchun, China.MaHangHState Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.QiYaodongYState Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.YeJingxueJState Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.LuMinMFaculty of Agronomy, Jilin Agricultural University, Changchun, China.WeiXuepingXState Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.engJournal ArticleSystematic Review20260319SwitzerlandFront Plant Sci1015682001664-462Xbibliometricsburst term analysiscore collectiongenetic diversitygenome-wide association studiesgermplasmmolecular markerThe author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.202512192026214202622720264612372026461236202646632026319epublish41937755PMC1304409510.3389/fpls.2026.1771164Agrama H. A., Yan W. G., Lee F., Fjellstrom R., Chen M. H., Jia M., et al. (2009). Genetic assessment of a mini-core subset developed from the usda rice genebank. Crop Sci. 49, 1336–1346. doi: 10.2135/cropsci2008.06.055110.2135/cropsci2008.06.0551Allender C. (2011). The second report on the state of the world’s plant genetic resources for food and agriculture. Rome: food and agriculture organization of the united nations, (2010), pp. 370, us $95.00, isbn 978-92-5-106534-1. Exp. Agric. 47, 574. doi: 10.1017/S0014479711000275, PMID:10.1017/S0014479711000275Ambati D., Phuke R. M., Vani V., Prasad S., Singh J. B., Patidar C. P., et al. (2020). Assessment of genetic diversity and development of core germplasm in durum wheat using agronomic and grain quality traits. Cereal Res. Commun. 48, 375–382. doi: 10.1007/s42976-020-00050-z, PMID:10.1007/s42976-020-00050-zAnglin N. L., Wenzl P., Azevedo V., Lusty C., Ellis D., Gao D. Y. (2025). Genotyping genebank collections: strategic approaches and considerations for optimal collection management. Plants 14, 252. doi: 10.3390/plants14020252, PMID:10.3390/plants14020252PMC1176834739861604
Anjani K., Raoof M. A., Prasad M., Duraimurugan P., Lucose C., Yadav P., et al. (2018).
Trait-specific accessions in global castor (ricinus communis l.) Germplasm core set for utilization in castor improvement. Ind. Crop Prod.
112, 766–774. doi: 10.1016/j.indcrop.2018.01.002, PMID:
10.1016/j.indcrop.2018.01.002Araus J. L., Kefauver S. C., Zaman-Allah M., Olsen M. S., Cairns J. E. (2018). Translating high-throughput phenotyping into genetic gain. Trends Plant Sci. 23, 451–466. doi: 10.1016/j.tplants.2018.02.001, PMID:10.1016/j.tplants.2018.02.001PMC593179429555431Balfourier F., Roussel V., Strelchenko P., Exbrayat-Vinson F., Sourdille P., Boutet G., et al. (2007). A worldwide bread wheat core collection arrayed in a 384-well plate. Theor. Appl. Genet. 114, 1265–1275. doi: 10.1007/s00122-007-0517-1, PMID:10.1007/s00122-007-0517-117318494
Belaj A., Dominguez-García M. D., Atienza S. G., Urdíroz N. M., de la Rosa R., Satovic Z., et al. (2012).
Developing a core collection of olive (olea europaea l.) Based on molecular markers (darts, ssrs, snps) and agronomic traits. Tree Genet. Genomes.
8, 365–378. doi: 10.1007/s11295-011-0447-6, PMID:
10.1007/s11295-011-0447-6Bernad V., Al-Tamimi N., Langan P., Gillespie G., Dempsey T., Henchy J., et al. (2024). Unlocking the genetic diversity and population structure of the newly introduced two-row spring european heritage barley collection (exhibit). Front. Plant Sci. 15. doi: 10.3389/fpls.2024.1268847, PMID:10.3389/fpls.2024.1268847PMC1098774038571708Brindisi L. (2022). Genome wide association study (GWAS) for bacterial wilt resistance in the Capsicum core collection. WorldVeg Internship Report. Shanhua, Taiwan: World Vegetable Center.Brown A. (1995). The core collection at the crossroads. In: Hodgkin T., Brown A. H. D., van Hintum Th. J. L., Morales E. A. V., editors. Core Collections of Plant Genetic Resources. (Chichester, UK: John Wiley & Sons; ), 3–19.Chen C. M. (2006). Citespace ii: detecting and visualizing emerging trends and transient patterns in scientific literature. J. Am. Soc Inf. Sci. 57, 359–377. doi: 10.1002/asi.20317, PMID:10.1002/asi.20317Chen L., Fan X., Qi H., Chen S. G., Li R., Liu Y. J. (2025. a). Study on chemical diversity, antioxidant and antibacterial activities, and hacat cytotoxicity of camphora tenuipilis (a traditional aromatic plant from xishuangbanna). Plants 14, 3409. doi: 10.3390/plants14223409, PMID:10.3390/plants14223409PMC1265640541304560Chen S. Y., Chen J., Jiang C. Q., Yao R. T., Xue J. M., Bai Y. F., et al. (2022). Trends in research on forest ecosystem services in the most recent 20 years: a bibliometric analysis. Forests 13, 1087. doi: 10.3390/f13071087, PMID:10.3390/f13071087Chen S. X., Lin Z. C., Zhou D. G., Wang C. R., Li H., Yu R. B., et al. (2017). Genome-wide study of an elite rice pedigree reveals a complex history of genetic architecture for breeding improvement. Sci. Rep. 7, 45685. doi: 10.1038/srep45685, PMID:10.1038/srep45685PMC537948628374863
Chen X., Liu Y. Y., Zheng F. S., Cheng G. X., Guo M., Li J. S., et al. (2025. b).
Construction of a core collection of tomato (Solanum lycopersicum) germplasm based on phenotypic traits and SNP markers. Sci. Hortic.
339, 113855. doi: 10.1016/j.scienta.2024.113855, PMID:
10.1016/j.scienta.2024.113855Chen J., Zavala C., Ortega N., Petroli C., Franco J., Burgueño J., et al. (2016). The development of quality control genotyping approaches: a case study using elite maize lines. PloS One 11, e157236. doi: 10.1371/journal.pone.0157236, PMID:10.1371/journal.pone.0157236PMC490065827280295Chu Y., Ramos L., Holbrook C. C., Ozias-Akins P. (2007). Frequency of a loss-of-function mutation in oleoyl-pc desaturase (ahfad2a) in the mini-core of the us peanut germplasm collection. Crop Sci. 47, 2372–2378. doi: 10.2135/cropsci2007.02.011710.2135/cropsci2007.02.0117Corrado G., Caramante M., Piffanelli P., Rao R. (2014). Genetic diversity in italian tomato landraces: implications for the development of a core collection. Sci. Hortic. 168, 138–144. doi: 10.1016/j.scienta.2014.01.027, PMID:10.1016/j.scienta.2014.01.027Das S., Upadhyaya H. D., Srivastava R., Bajaj D., Gowda C., Sharma S., et al. (2015). Genome-wide insertion-deletion (indel) marker discovery and genotyping for genomics-assisted breeding applications in chickpea. DNA Res. 22, 377–386. doi: 10.1093/dnares/dsv020, PMID:10.1093/dnares/dsv020PMC459640326385353De Beukelaer H., Davenport G. F., Fack V. (2018). Core hunter 3: flexible core subset selection. BMC Bioinf. 19, 203. doi: 10.1186/s12859-018-2209-z, PMID:10.1186/s12859-018-2209-zPMC609271929855322Dervishi A., Jakse J., Ismaili H., Javornik B., Stajner N. (2021). Genetic structure and core collection of olive germplasm from Albania revealed by microsatellite markers. Genes 12, 256. doi: 10.3390/genes12020256, PMID:10.3390/genes12020256PMC791661633578843Egan L. M., Hofmann R. W., Barrett B. A., Ghamkhar K., Hoyos Villegas V. (2019. a). Identification of founding accessions and patterns of relatedness and inbreeding derived from historical pedigree data in a white clover germplasm collection in New Zealand. Crop Sci. 59, 2087–2099. doi: 10.2135/cropsci2018.11.068810.2135/cropsci2018.11.0688Egan L. M., Hofmann R. W., Ghamkhar K., Hoyos Villegas V. (2019. b). Identification of founding accessions and patterns of relatedness and inbreeding derived from historical pedigree data in a red clover germplasm collection in New Zealand. Crop Sci. 59, 2100–2108. doi: 10.2135/cropsci2019.01.004510.2135/cropsci2019.01.0045Etminan A., Pour-Aboughadareh A., Mohammadi R., Ahmadi-Rad A., Moradi Z., Mahdavian Z., et al. (2017). Evaluation of genetic diversity in a mini core collection of Iranian durum wheat germplasms. J. Anim. Plant Sci. 27(5):1582–1587.FAO (2025). The Third Report on the State of the World’s Plant Genetic Resources for Food and Agriculture (Rome: FAO Commission on Genetic Resources for Food and Agriculture Assessments; ). doi: 10.4060/cd4711en10.4060/cd4711enFrankel O. H. (1984). Genetic perspectives of germplasm conservation. In: Arber W., Llimensee K., Peacock W. J., Starlinger P., editors. Genetic manipulation: Impact on man and society. (Cambridge: Cambridge University Press; ) 161–170.
Freitag N., Schneider D., Mir C., Stamp P., Hund A., Messmer R. (2012).
Swiss maize (Zea mays L.) landraces. Their genetic diversity and distinctiveness in a global comparison. Maydica
57, 226–235.
Ge G. L., Chen X. Y., Ma H. X., Zhang X. Q., Shi J. J., Wang X. X., et al. (2024). Bibliometric analysis of research trends in agricultural soil organic carbon components from 2000 to 2023. Front. Plant Sci. 15. doi: 10.3389/fpls.2024.1457826, PMID:10.3389/fpls.2024.1457826PMC1165214339698450
Girma G., Bhattacharjee R., Lopez-Montes A., Gueye B., Ofodile S., Franco J., et al. (2018).
Re-defining the yam (dioscorea spp.) Core collection using morphological traits. Plant Genet. Resour.-Charact. Util.
16, 193–200. doi: 10.1017/S1479262117000144, PMID:
10.1017/S1479262117000144
Gopal J., Kumar V., Kumar R., Mathur P. (2013).
Comparison of different approaches to establish a core collection of andigena (solanum tuberosum group andigena) potatoes. Potato Res.
56, 85–98. doi: 10.1007/s11540-013-9232-2, PMID:
10.1007/s11540-013-9232-2Gouesnard B., Bataillon T. M., Decoux G., Rozale C., Schoen D. J., David J. L. (2001). Mstrat: an algorithm for building germ plasm core collections by maximizing allelic or phenotypic richness. J. Hered. 92, 93–94. doi: 10.1093/jhered/92.1.93, PMID:10.1093/jhered/92.1.9311336240Gouy M., Bogard M., Mohamadi F., Demenou B. (2025). Optimizing core collections for genetic studies: a worldwide flax germplasm case study. Front. Plant Sci. 16. doi: 10.3389/fpls.2025.1675815, PMID:10.3389/fpls.2025.1675815PMC1259984341220777
Guo Q., Liu J., Li J. K., Cao S., Zhang Z. J., Zhang J. T., et al. (2022).
Genetic diversity and core collection extraction of Robinia pseudoacacia L. germplasm resources based on phenotype, physiology, and genotyping markers. Ind. Crop. Prod.
178, 114627. doi: 10.1016/j.indcrop.2022.114627, PMID:
10.1016/j.indcrop.2022.114627
Hu B. L., Wan Y., Li X., Lei J. G., Luo X. D., Yan W. G., et al. (2012).
Analysis on genetic diversity of phenotypic traits in rice (Oryza sativa) core collection and its comprehensive assessment. Acta Agron. Sin.
38, 829–839. doi: 10.3724/SP.J.1006.2012.00829, PMID:
10.3724/SP.J.1006.2012.00829Huang D. Q., Zhou S. C., Wang C. R., Li H., Wang Z. D., Zhou D. G., et al. (2022). Breeding of high-quality rice variety Huangguangyouzhan and its breeding effects. Commun. Agric. Sci. Technol. 6, 256–259.Jia H., Li X., Song J., Lin Y., Zhang X., Qiu Y., et al. (2021). Genome-wide association study of powdery mildew resistance of cucumber core germplasms. Acta Hortic. Sin. 48, 1371–1385. doi: 10.16420/j.issn.0513-353x.2021-020510.16420/j.issn.0513-353x.2021-0205
Jiang H. F., Ren X. P., Chen Y. N., Huang L., Zhou X. J., Huang J. Q., et al. (2013).
Phenotypic evaluation of the chinese mini-mini core collection of peanut (arachis hypogaea l.) And assessment for resistance to bacterial wilt disease caused by ralstonia solanacearum. Plant Genet. Resour.-Charact. Util.
11, 77–83. doi: 10.1017/S1479262112000408, PMID:
10.1017/S1479262112000408
Kashiwagi J., Krishnamurthy L., Gaur P. M., Upadhyaya H. D., Varshney R. K., Tobita S. (2013).
Traits of relevance to improve yield under terminal drought stress in chickpea (c. Arietinum l.). Field Crops Res.
145, 88–95. doi: 10.1016/j.fcr.2013.02.011, PMID:
10.1016/j.fcr.2013.02.011
Kashiwagi J., Krishnamurthy L., Upadhyaya H. D., Krishna H., Chandra S., Vadez V., et al. (2005).
Genetic variability of drought-avoidance root traits in the mini-core germplasm collection of chickpea (cicer arietinum l.). Euphytica
146, 213–222. doi: 10.1007/s10681-005-9007-1, PMID:
10.1007/s10681-005-9007-1Kim K. W., Chung H. K., Cho G. T., Ma K. H., Chandrabalan D., Gwag J. G., et al. (2007). Powercore: a program applying the advanced m strategy with a heuristic search for establishing core sets. Bioinformatics 23, 2155–2162. doi: 10.1093/bioinformatics/btm313, PMID:10.1093/bioinformatics/btm31317586551Krusenbaum L., Wissuwa M. (2025). Low-call-rate SNPs and presence-absence variation identified in the rice pan-genome can improve genomic prediction of rice gene bank accessions. Theor. Appl. Genet. 138, 295. doi: 10.1007/s00122-025-05080-x, PMID:10.1007/s00122-025-05080-xPMC1259468241201653
Kumar V., Singh A., Mithra S., Krishnamurthy S. L., Parida S. K., Jain S., et al. (2015).
Genome-wide association mapping of salinity tolerance in rice (oryza sativa). DNA Res.
22, 133–145. doi: 10.1093/dnares/dsu046, PMID:
10.1093/dnares/dsu046PMC440132425627243Labiszak B., Wachowiak W. (2025). Adaptive potential and genomic vulnerability of keystone forest tree species to climate change: a case study in Scots pine. Evol. Appl. 18, e70180. doi: 10.1111/eva.70180, PMID:10.1111/eva.70180PMC1268042841357564Lee H., Ro N., Jeong H., Kwon J., Jo J., Ha Y., et al. (2016). Genetic diversity and population structure analysis to construct a core collection from a large capsicum germplasm. BMC Genet. 17, 1–13. doi: 10.1186/s12863-016-0452-8, PMID:10.1186/s12863-016-0452-8PMC510981727842492Li P. R., Su T. B., Yu S. C., Wang H. P., Wang W. H., Yu Y. J., et al. (2019). Identification and development of a core set of informative genic snp markers for assaying genetic diversity in chinese cabbage. Hortic. Environ. Biotechnol. 60, 411–425. doi: 10.1007/s13580-019-00138-4, PMID:10.1007/s13580-019-00138-4Li C. H., Wang J., Chien D. H., Chujoy E., Song B. F., VanderZaag P. (2011). Cooperation-88: a high yielding, multi-purpose, late blight resistant cultivar growing in southwest China. Am. J. Potato Res. 88, 190–194. doi: 10.1007/s12230-010-9174-z, PMID:10.1007/s12230-010-9174-zLi X. B., Yan W. G., Agrama H., Jia L. M., Shen X. H., Jackson A., et al. (2011). Mapping qtls for improving grain yield using the usda rice mini-core collection. Planta 234, 347–361. doi: 10.1007/s00425-011-1405-0, PMID:10.1007/s00425-011-1405-021479810Li G. S., Zhang L. J., Bai C. K. (2012). Chinese cornus officinalis: genetic resources, genetic diversity and core collection. Genet. Resour. Crop Evol. 59, 1659–1671. doi: 10.1007/s10722-011-9789-z, PMID:10.1007/s10722-011-9789-zLi Z. C., Zhang H. L., Chao Y. S., Qiu Z. E., Wei X. H. (2003). Studies on the sampling strategy for primary core collection of Chinese ingenious rice. Acta Agron. Sin. 29, 20–24.Li H., Zhou S. C., Huang D. Q., Lu D. C., Wang J. S., Miao R. W. (2005). An application example of core collection-based breeding theory: Development of a high-quality rice variety Fengsizhan. China Rice 11, 19–20.Liang W., Dondini L., De Franceschi P., Paris R., Sansavini S., Tartarini S. (2015). Genetic diversity, population structure and construction of a core collection of apple cultivars from italian germplasm. Plant Mol. Biol. Rep. 33, 458–473. doi: 10.1007/s11105-014-0754-9, PMID:10.1007/s11105-014-0754-9Ma Z. Y., He S. P., Wang X. F., Sun J. L., Zhang Y., Zhang G. Y., et al. (2018). Resequencing a core collection of upland cotton identifies genomic variation and loci influencing fiber quality and yield. Nat. Genet. 50, 803. doi: 10.1038/s41588-018-0119-7, PMID:10.1038/s41588-018-0119-729736016Mahapatra M. (1985). On the validity of the theory of exponential growth of scientific literature. In: Proceedings of the 15th IASLIC Conference. (Bangalore, India: Indian Association of Special Libraries and Information Centres (IASLIC)) 61–70.Mahapatra G. (1994). Correlation between growth of publications and citations: a study based on growth curves. Ann. Libr. Sci. Doc. 41(1):8–12. (Delhi, India: Indian National Scientific Documentation Centre; ).Mahuku G. S., Jara C., Cajiao C., Beebe S. (2003). Sources of resistance to angular leaf spot (phaeoisariopsis griseola) in common bean core collection, wild phaseolus vulgaris and secondary gene pool. Euphytica 130, 303–313. doi: 10.1023/A:1023095531683, PMID:10.1023/A:1023095531683Ministry of Agriculture and Rural Affairs (MARA) (2024). Outstanding crop germplasm resources from the third national census of crop germplasm resources (Beijing: MARA; ).
Mouhaddab J., Ait Aabd N., Msanda F., Filali-Maltouf A., Belkadi B., Ferradous A., et al. (2016).
Assessing genetic diversity and constructing a core collection of an endangered Moroccan endemic tree [Argania spinosa (L.) Skeels]. Moroccan J. Biol.
13, 1–12.
Mu S. L., Niu H. L., Zhang C. X., Liu H. X., He Z. G., Li Y. F. (2017). Construction of peanut primary core germplasm in Jilin Province by agronomic traits and SSR markers. J. Jilin Agric. Univ. 39, 392–402. doi: 10.13327/j.jjlau.2017.332510.13327/j.jjlau.2017.3325
Nie X. H., Wang Z. H., Liu N. W., Song L., Yan B. Q., Xing Y., et al. (2021).
Fingerprinting 146 chinese chestnut (castanea mollissima blume) accessions and selecting a core collection using ssr markers. J. Integr. Agric.
20, 1277–1286. doi: 10.1016/S2095-3119(20)63400-1
10.1016/S2095-3119(20)63400-1Oliveira M. F., Nelson R. L., Geraldi I. O., Cruz C. D., de Toledo J. (2010). Establishing a soybean germplasm core collection. Field Crops Res. 119, 277–289. doi: 10.1016/j.fcr.2010.07.021, PMID:10.1016/j.fcr.2010.07.021Omer A., Zhang C. H., Liu J., Shan Z. G. (2025). Comprehensive review of mapping climate change impacts on tea cultivation: bibliometric and content analysis of trends, influences, adaptation strategies, and future directions. Front. Plant Sci. 15. doi: 10.3389/fpls.2024.1542793, PMID:10.3389/fpls.2024.1542793PMC1180280339925372Pande S., Kishore G. K., Upadhyaya H. D., Rao J. N. (2006). Identification of sources of multiple disease resistance in mini-core collection of chickpea. Plant Dis. 90, 1214–1218. doi: 10.1094/PD-90-1214, PMID:10.1094/PD-90-121430781104Pérez-Vega E., Campa A., de la Rosa L., Giraldez R., Ferreira J. J. (2009). Genetic diversity in a core collection established from the main bean genebank in Spain. Crop Sci. 49, 1377–1386. doi: 10.2135/cropsci2008.07.040910.2135/cropsci2008.07.0409
Ping F. (2008).
Constructing a core collection for maize (zea mays l.) Landrace from wuling mountain region in China. Agric. Sci. China.
7, 1423–1432. doi: 10.1016/S1671-2927(08)60398-3, PMID:
10.1016/S1671-2927(08)60398-3Qiu L. J., Li Y. H., Guan R. X., Liu Z. X., Wang L. X., Chang R. Z. (2009). Establishment, representative testing and research progress of soybean core collection and mini core collection. Acta Agron. Sin. 35, 571–579. doi: 10.3724/SP.J.1006.2009.00571, PMID:10.3724/SP.J.1006.2009.00571
Razi S., Soleimani A., Zeinalabedini M., Vazifeshenas M. R., Martínez-Gómez P., Kermani A. M., et al. (2021).
Development of a multipurpose core collection of new promising Iranian pomegranate (Punica granatum L.) genotypes based on morphological and pomological traits. Horticulturae
7, 350. doi: 10.3390/horticulturae7100350, PMID:
10.3390/horticulturae7100350
Razzaq A., Saleem F., Wani S. H., Abdelmohsen S., Alyousef H. A., Abdelbacki A., et al. (2021).
De-novo domestication for improving salt tolerance in crops. Front. Plant Sci.
12. doi: 10.3389/fpls.2021.681367, PMID:
10.3389/fpls.2021.681367PMC848161434603347Rellosa M. C., Reaño R. A., Capilit G., de Guzman F. C., Ali J., Hamilton N., et al. (2014). The 3,000 rice genomes project. Gigascience 3, 7. doi: 10.1186/2047-217X-3-7, PMID:10.1186/2047-217X-3-7PMC403566924872877Sandhu N., Singh J., Singh G., Sethi M., Singh M. P., Pruthi G., et al. (2022). Development and validation of a novel core set of KASP markers for the traits improving grain yield and adaptability of rice under direct-seeded cultivation conditions. Genomics 114, 110269. doi: 10.1016/j.ygeno.2022.110269, PMID:10.1016/j.ygeno.2022.11026935065190
Schafleitner R., Lin C. Y., Lin Y. P., Wu T. H., Hung C. H., Phooi C. L., et al. (2021).
The World Vegetable Center okra (Abelmoschus esculentus) core collection as a source for flooding stress tolerance traits for breeding. Agriculture
11, 165. doi: 10.3390/agriculture11020165, PMID:
10.3390/agriculture11020165Sharma S., Upadhyaya H. D., Varshney R. K., Gowda C. (2013). Pre-breeding for diversification of primary gene pool and genetic enhancement of grain legumes. Front. Plant Sci. 4. doi: 10.3389/fpls.2013.00309, PMID:10.3389/fpls.2013.00309PMC374762923970889
Skroch P. W., Nienhuis J., Beebe S., Tohme J., Pedraza F. (1998).
Comparison of mexican common bean (phaseolus vulgaris l.) Core and reserve germplasm collections. Crop Sci.
38, 488–496. doi: 10.2135/cropsci1998.0011183X003800020036x
10.2135/cropsci1998.0011183X003800020036xStenhouse A., Fisher N., Lepschi B., Schmidt-Lebuhn A., Rodriguez J., Turco F., et al. (2025). A vision of human-ai collaboration for enhanced biological collection curation and research. Bioscience 75, 457–471. doi: 10.1093/biosci/biaf021, PMID:10.1093/biosci/biaf021PMC1234291440808993Upadhyaya H. D., Bajaj D., Narnoliya L., Das S., Kumar V., Gowda C., et al. (2016). Genome-wide scans for delineation of candidate genes regulating seed-protein content in chickpea. Front. Plant Sci. 7. doi: 10.3389/fpls.2016.00302, PMID:10.3389/fpls.2016.00302PMC480373227047499
Upadhyaya H. D., Dwivedi S. L., Gowda C., Singh S. (2007. a).
Identification of diverse germplasm lines for agronomic traits in a chickpea (cicer arietinum l.) Core collection for use in crop improvement. Field Crops Res.
100, 320–326. doi: 10.1016/j.fcr.2006.08.008, PMID:
10.1016/j.fcr.2006.08.008Upadhyaya H. D., Kashiwagi J., Varshney R. K., Gaur P. M., Saxena K. B., Krishnamurthy L., et al. (2012). Phenotyping chickpeas and pigeonpeas for adaptation to drought. Front. Physiol. 3. doi: 10.3389/fphys.2012.00179, PMID:10.3389/fphys.2012.00179PMC336563422675307Upadhyaya H. D., Ortiz R. (2001). A mini core subset for capturing diversity and promoting utilization of chickpea genetic resources in crop improvement. Theor. Appl. Genet. 102, 1292–1298. doi: 10.1007/s00122-001-0556-y, PMID:10.1007/s00122-001-0556-yUpadhyaya H. D., Reddy K. N., Gowda C., Singh S. (2007. b). Phenotypic diversity in the pigeonpea (cajanus cajan) core collection. Genet. Resour. Crop Evol. 54, 1167–1184. doi: 10.1007/s10722-006-9008-5, PMID:10.1007/s10722-006-9008-5van Eck N. J., Waltman L. (2010). Software survey: vosviewer, a computer program for bibliometric mapping. Scientometrics 84, 523–538. doi: 10.1007/s11192-009-0146-3, PMID:10.1007/s11192-009-0146-3PMC288393220585380Varshney R. K., Roorkiwal M., Sun S., Bajaj P., Chitikineni A., Thudi M., et al. (2021). A chickpea genetic variation map based on the sequencing of 3,366 genomes. Nature 599, 622. doi: 10.1038/s41586-021-04066-1, PMID:10.1038/s41586-021-04066-1PMC861293334759320Wang L. X., Guan Y., Guan R. X., Li Y. H., Ma Y. S., Dong Z. M., et al. (2006). Establishment of chinese soybean glycine max core collections with agronomic traits and ssr markers. Euphytica 151, 215–223. doi: 10.1007/s10681-006-9142-3, PMID:10.1007/s10681-006-9142-3Wang J., Hu J., Huang X., Xu S. (2008). Progress in constructing data and evaluating parameters of representativeness for plant core collection. Seed 27, 52–55. doi: 10.16590/j.cnki.1001-4705.2008.08.06010.16590/j.cnki.1001-4705.2008.08.060Wang F. Y., Li C. Y., Yu H. Z., Yang D. F., Ye J., Zhang L. (2025). Biosynthesis-based metabolomics analysis reveals chemical diversity between two salvia species. Front. Plant Sci. 16. doi: 10.3389/fpls.2025.1613313, PMID:10.3389/fpls.2025.1613313PMC1227119640688688
Wang W. H., Miyatake K., Saito T., Harada Y., Yamaguchi S., Koyama M., et al. (2023).
Acetylcholine content in 100 accessions from the worldwide eggplant (Solanum melongena L.) core collection. J. Food Compos. Anal.
119, 105233. doi: 10.1016/j.jfca.2023.105233, PMID:
10.1016/j.jfca.2023.105233
Wang H. X., Zhao S. G., Gao Y., Xuan L. C., Zhang Z. H. (2013).
A construction of the core-collection of Juglans regia L. based on AFLP molecular markers. Sci. Agric. Sin.
46, 4985–4995. doi: 10.3864/j.issn.0578-1752.2013.23.015, PMID:
10.3864/j.issn.0578-1752.2013.23.015
Website CGIAR Genebank Initiative (2019). Genebanks-Platform-Full-Proposal. Available online at: https://genebanks.cgiar.org/wp-content/uploads/2019/09/Genebanks-Platform-Full-Proposal.pdf (Accessed February 9, 2026).
Website Dinesh Abrol (2025). Takeaways From ITPGRFA Governing Body Meet: Benefit-Sharing From Plant Genetic Resources is at Risk. Available online at: https://thewire.in/agriculture/takeaways-from-itpgrfa-governing-body-meet-benefit-sharing-from-plant-genetic-resources-is-at-risk (Accessed February 9, 2026).
Website Marcel Bruins (2025). Is Multilateralism Dead? The Lima Breakdown and the Future of Global Seed Cooperation. Available online at: https://www.seedworld.com/europe/2025/12/17/is-multilateralism-dead-the-lima-breakdown-and-the-future-of-global-seed-cooperation (Accessed February 10, 2026).
Website National Plant Germplasm System (NPGS) (2025). GRIN-Global Database. Beltsville, MD:
United States Department of Agriculture, Agricultural Research Service. Available online at: https://npgsweb.ars-grin.gov/gringlobal/search (Accessed February 8, 2026).
Website Protect and promote Crop Wild Relatives (Pro-Wild) (2025). At the crossroads plant treaty negotiations in lima end without a decision. Available online at: https://www.pro-wild.eu/news/at-the-crossroads-plant-treaty-negotiations-in-lima-end-without-a-decision.html (Accessed February 9, 2026).
Wei X., Qiu J., Yong K. C., Fan J. J., Zhang Q., Hua H., et al. (2021). A quantitative genomics map of rice provides genetic insights and guides breeding. Nat. Genet. 53, 243. doi: 10.1038/s41588-020-00769-9, PMID:10.1038/s41588-020-00769-933526925Wu Z. X., Wang T. Z., Chen J. J., Zhang Y., Lv G. H. (2024). Sweet corn association panel and genome-wide association analysis reveal loci for chilling-tolerant germination. Sci. Rep. 14, 10791. doi: 10.1038/s41598-024-61797-7, PMID:10.1038/s41598-024-61797-7PMC1108870038734751Xia X. Z., Zeng Y., Nong B. X., Deng G. F., Li D. T. (2014). Identification and evaluation of cold tolerance in core collection of landrace rice resources in Guangxi. Southwest China J. Agric. Sci. 27(5):1801–1805.Xu T. T., Lin F., Hua W., Wang J. M., Zhu J. H., Jia Q. J., et al. (2011). Establishment of a core collection of bred cultivars of Chinese barley. Acta Agric. Zhejiangensis 23(3):483–488.Xu H. M., Qiu Y. X., Hu J., Wang J. C. (2004). Methods of constructing core collection of crop germplasm by comparing different genetic distances, cluster methods and sampling strategies. Acta Agron. Sin. 30(9):932–936.Yuan Z. H., Rembe M., Mascher M., Stein N., Himmelbach A., Jayakodi M., et al. (2025). High-quality phenotypic and genotypic dataset of barley genebank core collection to unlock untapped genetic diversity. Gigascience 14, giae121. doi: 10.1093/gigascience/giae121, PMID:10.1093/gigascience/giae121PMC1181152639931028Yuan Z. H., Zhao Y. S., Oldach K., Jahoor A., Jensen J. D., Dohrendorf V. E., et al. (2026). Targeted expansion of a barley genebank core collection facilitates the discovery of disease resistance loci. Theor. Appl. Genet. 139, 30. doi: 10.1007/s00122-025-05139-9, PMID:10.1007/s00122-025-05139-9PMC1279107541520298Zewdie Y., Tong N., Bosland P. (2004). Establishing a core collection of capsicum using a cluster analysis with enlightened selection of accessions. Genet. Resour. Crop Evol. 51, 147–151. doi: 10.1023/B:GRES.0000020858.96226.38, PMID:10.1023/B:GRES.0000020858.96226.38Zhang X., Jiang Q., Shen Y., Wang H., Yao X. (2023). Using landscape genomics to assess local adaptation of fruit trees to current and future climatic conditions. Fruit Res. 4, e003. doi: 10.48130/frures-0023-003910.48130/frures-0023-0039Zhao R. R., Luo R. X., Ding X., Cui J., Yi B. J. (2025). Lightweight YOLOv11n-based detection and counting of early-stage cabbage seedlings from UAV RGB imagery. Horticulturae 11, 993. doi: 10.3390/horticulturae11080993, PMID:10.3390/horticulturae11080993Zhou S. C., Ke W., Miao R. W., Li H., Huang D. Q., Wang C. R. (2021). Creation and application of the breeding theory based on rice core germplasm. Chin. J. Rice Sci. 35, 529–534. doi: 10.16819/j.1001-7216.2021.21080110.16819/j.1001-7216.2021.210801Zhou S. C., Wang C. R., Zhao L., Li H., Huang D. Q., Wang Z. D., et al. (2019). Pedigree and breeding utilization of a series of good quality restorers derived from Fengbazhan in hybrid rice. Hybrid Rice 34, 6–13. doi: 10.16267/j.cnki.1005-3956.20181112.29310.16267/j.cnki.1005-3956.20181112.293Zong X. X., Redden R. J., Liu Q. C., Wang S. M., Guan J. P., Liu J., et al. (2009). Analysis of a diverse global pisum sp collection and comparison to a chinese local p. Sativum collection with microsatellite markers. Theor. Appl. Genet. 118, 193–204. doi: 10.1007/s00122-008-0887-z, PMID:10.1007/s00122-008-0887-z1881576841936088202604051873-42942026Mar18Current topics in medicinal chemistryCurr Top Med ChemBlue Biotech Bounty: Marine-Derived Fungi as a Potential Source of Antibacterial Agents.10.2174/0115680266407080251203144029The rise of antibiotic resistance has created an urgent need for novel antimicrobial agents. Marine ecosystems, particularly marine-derived fungi, have emerged as a rich resource of structurally diverse and complex secondary biometabolites demonstrating promising antibacterial capabilities. These microorganisms thrive in extreme environments, facilitating the synthesis of unique biologically active molecules not commonly found in terrestrial sources. The present review attempts to furnish a bird's eye view of antibacterial compounds derived from marine fungi, highlighting their structural diversity and pharmacological attributes for potential use against drugresistant pathogens. A study of relevant literature has been conducted using Google Scholar, a freely accessible web search engine, and various databases, including PubMed, Web of Science (WOS), Scopus, ScienceDirect, and SciFinder. Published literatures on marine fungal metabolites with antibacterial properties were analyzed, focusing on their chemical classes, fungal sources, and bioactivity against clinically relevant bacteria. Additionally, the influence of marine habitats on metabolite production and on innovative discovery approaches, such as co-cultivation, was examined. The review provides an overview of the classification of ocean-sourced fungi and explores various mechanisms of action of antibacterial compounds derived from marine fungi, including blocking cell wall formation, damaging bacterial membranes, inhibiting protein synthesis, and interfering with quorum sensing. It provides an extensive overview of diverse biologically active compounds obtained from oceanic fungi belonging to Aspergillus, Penicillium, Cladosporium, and other genera, with antibacterial potential. These metabolites encompass polyketides, alkaloids, terpenoids, peptides, diketopiperazines, and other compound classes identified from marinederived fungi. Notably, several compounds have demonstrated selective activity against drugresistant pathogens, emphasizing their pharmaceutical significance. Additionally, the review highlights the advantages of marine fungi-derived antibacterial agents and addresses the challenges associated with their discovery and development. Marine fungi serve as a promising yet underexplored resource for antibiotic discovery. This review serves as a valuable resource for researchers, summarizing key findings and offering insights into promising marine fungal metabolites. Moreover, it guides future research by identifying existing knowledge gaps and highlighting cutting-edge approaches to optimizing the identification and development of antibacterial compounds derived from marine fungi.Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.TripathiRati Kailash PrasadRKP0000-0002-9699-4155Department of Pharmaceutical Sciences, Sushruta School of Medical and Paramedical Sciences, Assam University (A Central University), Silchar, Assam 788011, India.engJournal Article20260318United Arab EmiratesCurr Top Med Chem1011196731568-0266IMMarine fungiadvantagesantibacterial compoundschallengesdrug resistancemechanisms of actionsecondary metabolites20255172025952025915202645183720264518372026451333aheadofprint4193608810.2174/0115680266407080251203144029CTMC-EPUB-1538314193425020260404202604041468-31483922026MarJournal of applied research in intellectual disabilities : JARIDJ Appl Res Intellect DisabilSocial Cognition in Toddlers, Children, and Adolescents With Down Syndrome: A Scoping Review.e70215e7021510.1111/jar.70215Social cognition (SC) involves mental processes underlying interactions. Children and adolescents with Down syndrome face sociocognitive challenges affecting relationships and well-being. This review systematically examines research on SC development in persons with Down syndrome.Following PRISMA-ScR guidelines, we systematically searched WoS and Scopus for peer-reviewed studies on SC in individuals with Down syndrome (0-19 years). Forty studies (2008-2024) met the criteria and were analysed and assessed for quality using the MMAT tool.Evidence does not clearly indicate age-related SC development in Down syndrome. Instead, language is the key factor influencing SC development. Individuals with Down syndrome show challenges in SC and emotion processing, specifically compared to typical development.More research is needed, especially longitudinal and developmental trajectory studies, to clarify SC development in Down syndrome and its relation to age. These findings can guide educational and clinical strategies to improve social inclusion and well-being.© 2026 John Wiley & Sons Ltd.Muñoz-MontesMagdalenaM0000-0002-9463-9731Faculty of Education, Pontificia Universidad Católica de Chile, Santiago, Chile.Department of Speech and Language Therapy, School of Health Sciences, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.MendiveSusanaSFaculty of Education, Pontificia Universidad Católica de Chile, Santiago, Chile.Centro de Interés Nacional para Investigación e Innovación en Niñez, Adolescencia, Resiliencia y Adversidad (IINARA), Santiago, Chile.TenorioMarcelaM0000-0003-0859-6462Millennium Institute for Care Research (MICARE), Santiago, Chile.Facultad de Educación, Centro de Investigación para la Mejora de los Aprendizajes (CIMA), Universidad del Desarrollo, Santiago, Chile.ArangoPaulinaP0000-0002-9942-6997Millennium Institute for Care Research (MICARE), Santiago, Chile.Institute of Public Health, Universidad Nacional Andrés Bello, Santiago, Chile.AparicioAndrésA0000-0002-0770-214XMillennium Institute for Care Research (MICARE), Santiago, Chile.WettlingIgnacioIDepartment of Speech and Language Therapy, School of Health Sciences, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.eng1221400Fondo Nacional de Desarrollo Científico y Tecnológico1221349Fondo Nacional de Desarrollo Científico y TecnológicoICS2019_024ANID Millennium Science Initiative ProgramCIN250068Centro de Interés Nacional IINARAJournal ArticleScoping ReviewReviewEnglandJ Appl Res Intellect Disabil96136161360-2322IMHumansDown SyndromepsychologyphysiopathologyChildAdolescentChild, PreschoolSocial CognitionInfantChild DevelopmentphysiologyYoung AdultThe literature does not indicate a clear relationship between social cognition and age in children and adolescents with Down syndrome. Language skills are the main predictor of theory of mind and emotion processing in children and adolescents with Down syndrome. Individuals with Down syndrome face challenges in emotion processing compared to typically developing peers. More longitudinal and developmental trajectory studies are needed to guide educational and clinical strategies that will improve social inclusion and well‐being at different life stages.© 2026 John Wiley & Sons Ltd.Down syndromeemotion processingintellectual disabilitysocial cognitiontheory of mind2026262025492026312202645420202645419202644613ppublish4193425010.1111/jar.70215ReferencesAbbeduto, L., N. Arias‐Trejo, A. John, J. Ramos‐Sánchez, and L. del Hoyo. 2023. “Language Development in Down Syndrome.” In The Oxford Handbook of Down Syndrome and Development, edited by J. A. Burack, J. O. Edgin, and L. Abbeduto, 332–366. Oxford University Press.Abbeduto, L., S. F. Warren, and F. A. Conners. 2007. “Language Development in Down Syndrome: From the Prelinguistic Period to the Acquisition of Literacy.” Mental Retardation and Developmental Disabilities Research Reviews 13: 247–261. https://doi.org/10.1002/mrdd.20158.Alevriadou, A., and S. Giaouri. 2011. “Second‐Order Mental State Attribution in Children With Intellectual Disability: Cognitive Functioning and Some Educational Planning Challenges.” Journal of Educational and Developmental Psychology 1, no. 1: 146. https://doi.org/10.5539/jedp.v1n1p146.Alkire, D., K. R. Warnell, L. A. Kirby, D. Moraczewski, and E. Redcay. 2021. “Explaining Variance in Social Symptoms of Children With Autism Spectrum Disorder.” Journal of Autism and Developmental Disorders 51, no. 4: 1249–1265. https://doi.org/10.1007/s10803‐020‐04598‐x.Altan Akin, N. T., and M. Gokturk. 2019. “Comparison of the Theory of Mind Tests on the Paper, 2D Touch Screen and Augmented Reality Environments on the Students With Neurodevelopmental Disorders.” IEEE Access 7: 52390–52404. https://doi.org/10.1109/ACCESS.2019.2902836.Amadó, A., E. Serrat, and E. Valles‐Majoral. 2016. “The Role of Executive Functions in Social Cognition Among Children With Down Syndrome: Relationship Patterns.” Frontiers in Psychology 7: 1363. https://doi.org/10.3389/fpsyg.2016.01363.Amadó, A., F. Sidera, and E. Serrat. 2022. “Affective and Cognitive Theory of Mind in Children With Down Syndrome: A Brief Report.” International Journal of Disability, Development and Education 71: 180–188. https://doi.org/10.1080/1034912X.2022.2095355.American Psychiatric Association. 2013. Diagnostic and Statistical Manual of Mental Disorders. 5th ed. American Psychiatric Publishing.Annaz, D., A. Karmiloff‐Smith, M. H. Johnson, and M. S. C. Thomas. 2009. “A Cross‐Syndrome Study of the Development of Holistic Face Recognition in Children With Autism, Down Syndrome, and Williams Syndrome.” Journal of Experimental Child Psychology 102, no. 4: 456–486. https://doi.org/10.1016/j.jecp.2008.11.005.Arango, P. S., A. Aparicio, and M. Tenorio. 2018. “Developmental Trajectories of Children With Down Syndrome by Socio‐Economic Status: The Case of Latin America.” Journal of Intellectual Disability Research 62, no. 9: 759–774. https://doi.org/10.1111/jir.12516.Aromataris, E., C. Lockwood, K. Porritt, B. Pilla, and Z. Jordan. 2024. JBI Manual for Evidence Synthesis. JBI. https://doi.org/10.46658/JBIMES‐24‐01.Ashby, S. A., M. M. Channell, and L. Abbeduto. 2017. “Inferential Language Use by Youth With Down Syndrome During Narration.” Research in Developmental Disabilities 71: 98–108. https://doi.org/10.1016/j.ridd.2017.10.002.Azabdaftari, F., and F. Azabdaftari. 2008. “Social Impairment in Childhood Autism and Theory of Mind.” Rawal Medical Journal 33, no. 1: 79–81. https://www.rmj.org.pk/?mno=7547.Barisnikov, K., and F. Lejeune. 2018. “Social Knowledge and Social Reasoning Abilities in a Neurotypical Population and in Children With Down Syndrome.” PLoS One 13, no. 7: e0200932. https://doi.org/10.1371/journal.pone.0200932.Barisnikov, K., A. Theurel, and F. Lejeune. 2020. “Emotion Knowledge in Neurotypical Children and in Those With Down Syndrome.” Applied Neuropsychology: Child 11, no. 3: 197–211. https://doi.org/10.1080/21622965.2020.1777131.Barisnikov, K., A. Theurel, and F. Lejeune. 2022. “Emotion Knowledge in Neurotypical Children and in Those With Down Syndrome.” Applied Neuropsychology: Child 11, no. 3: 197–211. https://doi.org/10.1080/21622965.2020.1777131.Barisnikov, K., M. Thomasson, J. Stutzmann, and F. Lejeune. 2020. “Relation Between Processing Facial Identity and Emotional Expression in Typically Developing School‐Age Children and Those With Down Syndrome.” Applied Neuropsychology: Child 9, no. 2: 179–192. https://doi.org/10.1080/21622965.2018.1552867.Baron‐Cohen, S., A. M. Leslie, and U. Frith. 1985. “Does the Autistic Child Have a “Theory of Mind”?” Cognition 21, no. 1: 37–46. https://doi.org/10.1016/0010‐0277(85)90022‐8.Baron‐Cohen, S., S. Wheelwright, J. Hill, Y. Raste, and I. Plumb. 2001. “The “Reading the Mind in the Eyes” Test Revised Version: A Study With Normal Adults, and Adults With Asperger Syndrome or High‐Functioning Autism.” Journal of Child Psychology and Psychiatry 42, no. 2: 241–251. https://doi.org/10.1111/1469‐7610.00715.Beauchamp, M. H., and V. Anderson. 2010. “SOCIAL: An Integrative Framework for the Development of Social Skills.” Psychological Bulletin 136, no. 1: 39–64. https://doi.org/10.1037/a0017768.Beaudoin, C., É. Leblanc, C. Gagner, and M. H. Beauchamp. 2020. “Systematic Review and Inventory of Theory of Mind Measures for Young Children.” Frontiers in Psychology 10: 2905. https://doi.org/10.3389/fpsyg.2019.02905.Benton, A., K. Hamsher, N. R. Varney, and O. Spreen. 1983. Benton Test of Facial Recognition. Oxford University Press.Brüne, M., M. Abdel‐Hamid, C. Lehmkämper, and C. Sonntag. 2007. “Mental State Attribution, Neurocognitive Functioning, and Psychopathology: What Predicts Poor Social Competence in Schizophrenia Best?” Schizophrenia Research 92, no. 1: 151–159. https://doi.org/10.1016/j.schres.2007.01.006.Cacioppo, S., J. P. Capitanio, and J. T. Cacioppo. 2014. “Toward a Neurology of Loneliness.” Psychological Bulletin 140, no. 6: 1464–1504. https://doi.org/10.1037/a0037618.Cebula, K. R., D. G. Moore, and J. G. Wishart. 2010. “Social Cognition in Children With Down's Syndrome: Challenges to Research and Theory Building.” Journal of Intellectual Disability Research 54, no. 2: 113–134. https://doi.org/10.1111/j.1365‐2788.2009.01215.x.Cebula, K. R., and J. G. Wishart. 2008. “Social Cognition in Children With Down Syndrome.” International Review of Research in Mental Retardation 35: 43–86. https://doi.org/10.1016/S0074‐7750(07)35002‐7.Cebula, K. R., J. G. Wishart, D. S. Willis, and T. K. Pitcairn. 2017. “Emotion Recognition in Children With Down Syndrome: Influence of Emotion Label and Expression Intensity.” American Journal on Intellectual and Developmental Disabilities 122, no. 2: 138–155. https://doi.org/10.1352/1944‐7558‐122.2.138.Channell, M. M. 2020a. “Cross‐Sectional Trajectories of Mental State Language Development in Children With Down Syndrome.” American Journal of Speech‐Language Pathology 29, no. 2: 760–775. https://doi.org/10.1044/2020_AJSLP‐19‐00035.Channell, M. M. 2020b. “The Social Responsiveness Scale (SRS‐2) in School‐Age Children With Down Syndrome at Low Risk for Autism Spectrum Disorder.” Autism and Developmental Language Impairments 5: 239694152096240. https://doi.org/10.1177/2396941520962406.Channell, M. M., F. A. Conners, and J. M. Barth. 2014. “Emotion Knowledge in Children and Adolescents With Down Syndrome: A New Methodological Approach.” American Journal on Intellectual and Developmental Disabilities 119, no. 5: 405–421. https://doi.org/10.1352/1944‐7558‐119.5.405.Channell, M. M., L. J. Mattie, D. R. Hamilton, et al. 2021. “Capturing Cognitive and Behavioral Variability Among Individuals With Down Syndrome: A Latent Profile Analysis.” Journal of Neurodevelopmental Disorders 13, no. 1: 16. https://doi.org/10.1186/s11689‐021‐09365‐2.Channell, M. M., L. E. Sandstrom, and D. Harvey. 2022. “Mental State Language Development in Children With Down Syndrome Versus Typical Development.” American Journal on Intellectual and Developmental Disabilities 127, no. 6: 495–510. https://doi.org/10.1352/1944‐7558‐127.6.495.Chicoine, B., A. Rivelli, V. Fitzpatrick, L. Chicoine, G. Jia, and A. Rzhetsky. 2021. “Prevalence of Common Disease Conditions in a Large Cohort of Individuals With Down Syndrome in the United States.” Journal of Patient‐Centered Research and Reviews 8, no. 2: 86–97. https://doi.org/10.17294/2330‐0698.1824.Clarke, M. T., D. Loganathan, and J. Swettenham. 2012. “Assessing True and False Belief in Young Children With Cerebral Palsy Through Anticipatory Gaze Behaviours: A Pilot Study.” Research in Developmental Disabilities 33, no. 6: 2058–2066. https://doi.org/10.1016/j.ridd.2012.05.009.Comblain, A., and C. Schmetz. 2020. “Improving Theory of Mind Skills in Down Syndrome? A Pilot Study.” Journal of Cognitive Education and Psychology 19, no. 1: 20–31. https://doi.org/10.1891/JCEP‐D‐18‐00034.Constantino, J. N., and C. P. Gruber. 2012. The Social Responsiveness Scale. 2nd ed. Western Psychological Services.DeJong, H., F. Van den Eynde, H. Broadbent, et al. 2013. “Social Cognition in Bulimia Nervosa: A Systematic Review.” European Psychiatry 28, no. 1: 1–6. https://doi.org/10.1016/j.eurpsy.2011.07.002.del Hoyo Soriano, L., A. J. Thurman, and L. Abbeduto. 2018. “Specificity: A Phenotypic Comparison of Communication‐Relevant Domains Between Youth With Down Syndrome and Fragile X Syndrome.” Frontiers in Genetics 9: 424. https://doi.org/10.3389/fgene.2018.00424.Devine, R. T., and C. Hughes. 2016. “Measuring Theory of Mind Across Middle Childhood: Reliability and Validity of the Silent Films and Strange Stories Tasks.” Journal of Experimental Child Psychology 14, no. 9: 23–40. https://doi.org/10.1016/j.jecp.2015.07.011.Edgin, J. O., B. F. Pennington, and C. B. Mervis. 2010. “Neuropsychological Components of Intellectual Disability: The Contributions of Immediate, Working, and Associative Memory.” Journal of Intellectual Disability Research 54, no. 5: 406–417. https://doi.org/10.1111/j.1365‐2788.2010.01278.x.Er‐rida, S., A. Zaidouni, M. Mafhoum, M. Oubibi, M. Alami, and A. Mdaghri Alaoui. 2024. “Inclusive Education: Exploring Parental Aspirations for Children With Down Syndrome in Regular Schools.” Open Psychology Journal 17, no. 1: e18743501311174240906104333. https://doi.org/10.2174/0118743501311174240906104333.Escudero, S. C., and E. M. Sepúlveda. 2024. “Pragmatic Competence in People With Dual Diagnosis: Down Syndrome and Autism Spectrum Disorder.” BMC Psychology 12, no. 1: 74. https://doi.org/10.1186/s40359‐023‐01508‐5.Feldman‐Barrett, L., and P. Salovey, eds. 2002. The Wisdom in Feeling: Psychological Processes in Emotional Intelligence. Guilford Press.Fidler, D. J., D. E. Most, and A. D. Philofsky. 2009. “The Down Syndrome Behavioral Phenotype: Taking a Developmental Approach.” Down's Syndrome, Research and Practice 12: 37–44.Fidler, D. J., M. A. Prince, K. Van Deusen, et al. 2022. “Latent Profiles of Autism Symptoms in Children and Adolescents With Down Syndrome.” Journal of Intellectual Disability Research 66, no. 3: 265–281. https://doi.org/10.1111/jir.12910.Fidler, D. J., K. Van Deusen, M. A. Prince, et al. 2023. “Longitudinal Predictors of Neurodevelopmental Outcomes in Children With Down Syndrome.” Developmental Neuropsychology 48, no. 6: 280–298. https://doi.org/10.1080/87565641.2023.2239401.Fiske, S. T., and S. E. Taylor. 1991. Social Cognition. McGraw‐Hill Book Company.Freire, K., R. Pope, K. Jeffrey, K. Andrews, M. Nott, and T. Bowman. 2022. “Engaging With Children and Adolescents: A Systematic Review of Participatory Methods and Approaches in Research Informing the Development of Health Resources and Interventions.” Adolescent Research Review 7, no. 3: 335–354. https://doi.org/10.1007/s40894‐022‐00181‐w.Frith, C. D. 1992. The Cognitive Neuropsychology of Schizophrenia. Lawrence Erlbaum Associates, Inc.Frith, C. D. 2008. “Cognición Social.” Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 363: 2033–2039. https://doi.org/10.1098/rstb.0005.Frith, C. D., and U. Frith. 2007. “Social Cognition in Humans.” Current Biology 17, no. 16: 724–732. https://doi.org/10.1016/j.cub.2007.05.068.Fu, I.‐N., K.‐L. Chen, M.‐R. Liu, D.‐R. Jiang, C.‐L. Hsieh, and S.‐C. Lee. 2023. “A Systematic Review of Measures of Theory of Mind for Children.” Developmental Review 67: 1–27. https://doi.org/10.1016/j.dr.2022.101061.Gagliardi, C., E. Frigerio, D. M. Burt, I. Cazzaniga, D. I. Perrett, and R. Borgatti. 2003. “Facial Expression Recognition in Williams Syndrome.” Neuropsychologia 41, no. 6: 733–738. https://doi.org/10.1016/S0028‐3932(02)00178‐1.Green, M., B. Olivier, J. Crawley, D. Penn, and S. Silverstein. 2005. “Social Cognition in Schizophrenia: Recommendations From the Measurement and Treatment Research to Improve Cognition in Schizophrenia New Approaches Conference.” Schizophrenia Bulletin 31, no. 4: 882–887. https://doi.org/10.1093/schbul/sbi049.Green, M. F., D. L. Penn, R. Bentall, et al. 2008. “Social Cognition in Schizophrenia: An NIMH Workshop on Definitions, Assessment, and Research Opportunities.” Schizophrenia Bulletin 34, no. 6: 1211–1220. https://doi.org/10.1093/schbul/sbm145.Guralnick, M. J., R. T. Connor, and L. C. Johnson. 2011. “The Peer Social Networks of Young Children With Down Syndrome in Classroom Programmes.” Journal of Applied Research in Intellectual Disabilities 24, no. 4: 310–321. https://doi.org/10.1111/j.1468‐3148.2010.00619.x.Hahn, L., D. Fidler, S. Hepburn, and S. Rogers. 2013. “Early Intersubjective Skills and the Understanding of Intentionality in Young Children With Down Syndrome.” Research in Developmental Disabilities 34, no. 12: 4455–4465. https://doi.org/10.1016/j.ridd.2013.09.027.Hernandez‐Lara, M., A. I. Martinez‐Garcia, and K. Caro. 2023. “Using Emotion4Down: Evaluating the Design of a Serious Video Game for Supporting Emotional Awareness With People With Intellectual Disabilities.” Interacting With Computers 35, no. 2: 363–386. https://doi.org/10.1093/iwc/iwac044.Hessling, A., and D. M. Brimo. 2019. “Spoken Fictional Narrative and Literacy Skills of Children With Down Syndrome.” Journal of Communication Disorders 79: 76–89. https://doi.org/10.1016/j.jcomdis.2019.03.005.Hippolyte, L., K. Iglesias, M. Van der Linden, and K. Barisnikov. 2010. “Social Reasoning Skills in Adults With Down Syndrome: The Role of Language, Executive Functions and Socio‐Emotional Behaviour.” Journal of Intellectual Disability Research 54, no. 8: 714–726. https://doi.org/10.1111/j.1365‐2788.2010.01299.x.Hong, Q. N., S. Fàbregues, G. Bartlett, et al. 2018. “The Mixed Methods Appraisal Tool (MMAT) Version 2018 for Information Professionals and Researchers.” Education for Information 34, no. 4: 285–291. https://doi.org/10.3233/EFI‐180221.Jahromi, L., A. Gulsrud, and C. Kasari. 2008. “Emotional Competence in Children With Down Syndrome: Negativity and Regulation.” American Journal on Mental Retardation 113, no. 1: 32–43.Kalbe, E., M. Schlegel, A. T. Sack, et al. 2010. “Dissociating Cognitive From Affective Theory of Mind: A TMS Study.” Cortex 46, no. 6: 769–780. https://doi.org/10.1016/j.cortex.2009.07.010.Karmiloff‐Smith, A. 1992. Beyond Modularity: A Developmental Perspective on Cognitive Science. MIT Press.Karmiloff‐Smith, A. 1998. “Development Itself Is the Key to Understanding Developmental Disorders.” Trends in Cognitive Sciences 2: 389–398.Karmiloff‐Smith, A. 2009a. “A Developmental Perspective on Modularity.” In Towards a Theory of Thinking: Building Blocks for a Conceptual Framework, edited by B. M. Glatzeder, V. Goel, and A. von Müller, 179–187. Springer. https://doi.org/10.1007/978‐3‐540‐92846‐5_11.Karmiloff‐Smith, A. 2009b. “Nativism Versus Neuroconstructivism: Rethinking the Study of Developmental Disorders.” Developmental Psychology 45, no. 1: 56–63. https://doi.org/10.1037/a0014506.Karmiloff‐Smith, A., T. Al‐Janabi, H. D'Souza, et al. 2016. “The Importance of Understanding Individual Differences in Down Syndrome [Version 1; Peer Review: 2 Approved].” F1000Research 5: 389. https://doi.org/10.12688/f1000research.7506.1.Kim, Y.‐S. G., R. Dore, M. Cho, R. Golinkoff, and S. J. Amendum. 2021. “Theory of Mind, Mental State Talk, and Discourse Comprehension: Theory of Mind Process Is More Important for Narrative Comprehension Than for Informational Text Comprehension.” Journal of Experimental Child Psychology 209: 105181. https://doi.org/10.1016/j.jecp.2021.105181.Knowland, V., and M. Thomas. 2011. “Developmental Trajectories in Genetic Disorders.” International Review of Research in Developmental Disabilities 40: 43–73. https://doi.org/10.1016/B978‐0‐12‐374478‐4.00003‐4.Korkman, M., U. Kirk, and S. Kemp. 2007. NEPSY ‐Second Edition (NEPSY ‐ II). Harcourt Assessment.Korkman, M., U. Kirk, and S. Kemp. 2014. NEPSY‐II: Manual Clínico y de Interpretación. Pearson, The Psychological Corporation.Kunonga, T. P., K. H. Thomson, F. R. Beyer, S. Dawson, K. Brittain, and D. Craig. 2024. “A Mixed Methods Systematic Review of the Effectiveness, Acceptability, and Feasibility of Advanced Care Plans for Older Adults Without Life‐Limiting Conditions.” Journal of Long‐Term Care: 383–402. https://doi.org/10.31389/jltc.327.LaRusso, M., H. Y. Kim, R. Selman, et al. 2016. “Contributions of Academic Language, Perspective Taking, and Complex Reasoning to Deep Reading Comprehension.” Journal of Research on Educational Effectiveness 9, no. 2: 201–222. https://doi.org/10.1080/19345747.2015.1116035.Lee, M., L. Bush, G. E. Martin, et al. 2017. “A Multi‐Method Investigation of Pragmatic Development in Individuals With Down Syndrome.” American Journal on Intellectual and Developmental Disabilities 122, no. 4: 289–309. https://doi.org/10.1352/1944‐7558‐122.4.289.Lindquist, K. A., and M. Gendron. 2013. “What's in a Word? Language Constructs Emotion Perception.” Emotion Review 5, no. 1: 66–71. https://doi.org/10.1177/1754073912451351.Llewellyn, C., S. Ayers, I. McManus, et al. 2019. Cambridge Handbook of Psychology, Health and Medicine. Cambridge University Press.Losh, M., G. E. Martin, J. Klusek, A. L. Hogan‐Brown, and J. Sideris. 2012. “Social Communication and Theory of Mind in Boys With Autism and Fragile X Syndrome.” Frontiers in Psychology 3: 266. https://doi.org/10.3389/fpsyg.2012.00266.Mai, C. T., J. L. Isenburg, M. A. Canfield, et al. 2019. “National Population‐Based Estimates for Major Birth Defects, 2010–2014.” Birth Defects Research 111, no. 18: 1420–1435. https://doi.org/10.1002/bdr2.1589.Marchal, J. P., H. Maurice‐Stam, B. A. Houtzager, et al. 2016. “Growing Up With Down Syndrome: Development From 6 Months to 10.7 Years.” Research in Developmental Disabilities 59: 437–450. https://doi.org/10.1016/j.ridd.2016.09.019.Martínez‐Castilla, P., M. Burt, R. Borgatti, and C. Gagliardi. 2015. “Facial Emotion Recognition in Williams Syndrome and Down Syndrome: A Matching and Developmental Study.” Child Neuropsychology 21, no. 5: 668–692. https://doi.org/10.1080/09297049.2014.945408.Mayer, J. D., and P. Salovey. 1997. “What Is Emotional Intelligence?” In Emotional Development and Emotional Intelligence: Educational Implications, edited by P. Salovey and D. J. Sluyter, 3–34. Basic Books.Mayer, J. D., P. Salovey, D. R. Caruso, and G. Sitarenios. 2001. “Emotional Intelligence as a Standard Intelligence.” Emotion 1, no. 3: 232–242. https://doi.org/10.1037/1528‐3542.1.3.232.McDuffie, A., A. J. Thurman, M. M. Channell, and L. Abbeduto. 2017. “Language Disorders in Children With Intellectual Disability of Genetic Origin.” In Handbook of Child Language Disorders, edited by R. Schwartz, 2nd ed., 52–81. Taylor & Francis.Miller, S. A. 2022. Advanced Theory of Mind. Oxford University Press.Miller, S. E., and S. Marcovitch. 2012. “How Theory of Mind and Executive Function Co‐Develop.” Review of Philosophy and Psychology 3, no. 4: 597–625. https://doi.org/10.1007/s13164‐012‐0117‐0.Morel, A., E. Peyroux, A. Leleu, E. Favre, N. Franck, and C. Demily. 2018. “Overview of Social Cognitive Dysfunctions in Rare Developmental Syndromes With Psychiatric Phenotype.” Frontiers in Pediatrics 6: 102. https://doi.org/10.3389/fped.2018.00102.Nadel, L. 2003. “Down's Syndrome: A Genetic Disorder in Biobehavioral Perspective.” Genes, Brain and Behavior 2, no. 3: 156–166. https://doi.org/10.1034/j.1601‐183X.2003.00026.x.Næss, K. B., E. Nygaard, J. Ostad, A. S. Dolva, and S. H. Lyster. 2017. “The Profile of Social Functioning in Children With Down Syndrome.” Disability and Rehabilitation 39, no. 13: 1320–1331. https://doi.org/10.1080/09638288.2016.1194901.Ouzzani, M., H. Hammady, Z. Fedorowicz, and A. Elmagarmid. 2016. “Rayyan‐A Web and Mobile App for Systematic Reviews.” Systematic Reviews 5, no. 1: 210. https://doi.org/10.1186/s13643‐016‐0384‐4.Panzeri, F., B. Giustolisi, and L. Zampini. 2020. “The Comprehension of Ironic Criticisms and Ironic Compliments in Individuals With Down Syndrome: Adding Another Piece to the Puzzle.” Journal of Pragmatics 156: 223–234. https://doi.org/10.1016/j.pragma.2019.08.009.Pavlova, M. A., J. Galli, F. Pagani, et al. 2018. “Social Cognition in Down Syndrome: Face Tuning in Face‐Like Non‐Face Images.” Frontiers in Psychology 9: 2583. https://doi.org/10.3389/fpsyg.2018.02583.Pepi, A., and M. Alesi. 2005. “Attribution Style in Adolescents With Down's Syndrome.” European Journal of Special Needs Education 20, no. 4: 419–432. https://doi.org/10.1080/08856250500268650.Pino, M. C., R. Vagnetti, F. Masedu, et al. 2020. “Mapping the Network of Social Cognition Domains in Children With Autism Spectrum Disorder Through Graph Analysis.” Frontiers in Psychiatry 11: 579339. https://doi.org/10.3389/fpsyt.2020.579339.Plana, I., M.‐A. Lavoie, M. Battaglia, and A. M. Achim. 2014. “A Meta‐Analysis and Scoping Review of Social Cognition Performance in Social Phobia, Posttraumatic Stress Disorder and Other Anxiety Disorders.” Journal of Anxiety Disorders 28, no. 2: 169–177. https://doi.org/10.1016/j.janxdis.2013.09.005.Pochon, R., and C. Declercq. 2013. “Emotion Recognition by Children With Down Syndrome: A Longitudinal Study.” Journal of Intellectual and Developmental Disability 38, no. 4: 332–343. https://doi.org/10.3109/13668250.2013.826346.Pochon, R., and C. Declercq. 2014. “Emotional Lexicon Understanding and Emotion Recognition: A Longitudinal Study in Children With Down Syndrome.” Journal of Developmental and Physical Disabilities 26, no. 5: 549–563. https://doi.org/10.1007/s10882‐014‐9380‐6.Pochon, R., C. Touchet, and L. Ibernon. 2017. “Emotion Recognition in Adolescents With Down Syndrome: A Nonverbal Approach.” Brain Sciences 7, no. 6: 55. https://doi.org/10.3390/brainsci7060055.Reed, P., and I. Steed. 2015. “Interference With Facial Emotion Recognition by Verbal but Not Visual Loads.” Research in Developmental Disabilities 47: 441–450. https://doi.org/10.1016/j.ridd.2015.10.007.Reeves, R. H., and C. C. Garner. 2007. “A Year of Unprecedented Progress in Down Syndrome Basic Research.” Mental Retardation and Developmental Disabilities Research Reviews 13, no. 3: 215–220. https://doi.org/10.1002/mrdd.20165.Richards, C., C. Jones, L. Groves, J. Moss, and C. Oliver. 2015. “Prevalence of Autism Spectrum Disorder Phenomenology in Genetic Disorders: A Systematic Review and Meta‐Analysis.” Lancet Psychiatry 2, no. 10: 909–916. https://doi.org/10.1016/S2215‐0366(15)00376‐4.Richardson, F. M., and M. S. C. Thomas. 2015. “Language Development in Genetic Disorders.” In The Cambridge Handbook of Child Language, edited by E. Bavin, 459–471. Cambridge University Press.Rivelli, A., V. Fitzpatrick, S. Chaudhari, et al. 2022. “Prevalence of Mental Health Conditions Among 6078 Individuals With Down Syndrome in the United States.” Journal of Patient‐Centered Research and Reviews 9, no. 1: 58–63. https://doi.org/10.17294/2330‐0698.1875.Roid, G., and L. Miller. 1997. Leiter International Performance Scale‐Revised. Stoelting.Roid, G. H., L. J. Miller, M. Pomplun, and C. Koch. 2013. Leiter International Performance Scale. Third ed. Stoelting.Rosende‐Vázquez, M., and P. Vieiro‐Iglesias. 2013. “Los Procesos Inferenciales en Lectores Con Síndrome de Down.” Relieve 19, no. 1: 1–14. https://doi.org/10.7203/relieve.19.1.2345.Schlicht, T. 2023. Philosophy of Social Cognition. Palgrave Macmillan.Schworer, E., E. Hoffman, and A. Esbensen. 2021. “Psychometric Evaluation of Social Cognition and Behavior Measures in Children and Adolescents With Down Syndrome.” Brain Sciences 11, no. 7: 836. https://doi.org/10.3390/brainsci11070836.Schworer, E. K., A. J. Esbensen, V. Nguyen, et al. 2022. “Patterns and Predictors of Adaptive Skills in 2‐ to 7‐Year‐Old Children With Down Syndrome.” Journal of Neurodevelopmental Disorders 14, no. 1: 18. https://doi.org/10.1186/s11689‐022‐09430‐4.Shojaeian, N., Z. Li, R. P. S. Kaurav, and A. A. M. S. Salem. 2022. “Theory of Mind Among Swedish Children With ASD, Down Syndrome and Typically Developing Group.” Journal of Autism and Developmental Disorders 52, no. 11: 4774–4782. https://doi.org/10.1007/s10803‐021‐05366‐1.Shulman, C. 2023. “Co‐Occurrence of Autism Spectrum Disorder and Down Syndrome.” In The Oxford Handbook of Down Syndrome and Development, edited by J. A. Burack, J. O. Edgin, and L. Abbeduto, 417–442. Oxford University Press.Smolker, H. R., K. Wang, M. Luciana, et al. 2022. “The Emotional Word‐Emotional Face Stroop Task in the ABCD Study: Psychometric Validation and Associations With Measures of Cognition and Psychopathology.” Developmental Cognitive Neuroscience 53: 101054. https://doi.org/10.1016/j.dcn.2021.101054.Soccorso, C., M. Hojlo, K. Pawlowski, et al. 2024. “Development, Education, and Services in Children With Down Syndrome: A Cohort Analysis From a Clinical Database.” Frontiers in Psychology 15: 1348404. https://doi.org/10.3389/fpsyg.2024.1348404.Somogyi, E., I. Király, G. Gergely, and J. Nadel. 2013. “Understanding Goals and Intentions in Low‐Functioning Autism.” Research in Developmental Disabilities 34, no. 11: 3822–3832. https://doi.org/10.1016/j.ridd.2013.07.039.Steerneman, P., and C. Meesters. 2009. ToM Test‐R/Handleiding. Garant.Tahiroglu, D., L. J. Moses, S. M. Carlson, C. E. V. Mahy, E. L. Olofson, and M. A. Sabbagh. 2014. “The Children's Social Understanding Scale: Construction and Validation of a Parent‐Report Measure for Assessing Individual Differences in Children's Theories of Mind.” Developmental Psychology 50, no. 11: 2485–2497. https://doi.org/10.1037/a0037914.Tenorio, M., R. Campos, and A. Karmiloff‐Smith. 2014. “What Standardized Tests Ignore When Assessing Individuals With Neurodevelopmental Disorders.” Estudios de Psicología 35, no. 2: 426–437. https://doi.org/10.1080/02109395.2014.922264.Tenorio, M., J. Donoso, A. Ali, and A. Hassiotis. 2020. “Stigma Toward Persons With Intellectual Disability in South America: A Narrative Review.” Journal of Policy and Practice in Intellectual Disabilities 17, no. 4: 346–363. https://doi.org/10.1111/jppi.12357.Thomas, M. S. C., D. Annaz, D. Ansari, G. Scerif, C. Jarrold, and A. Karmiloff‐Smith. 2009. “Using Developmental Trajectories to Understand Developmental Disorders.” Journal of Speech Language and Hearing Research 52, no. 2: 336–358. https://doi.org/10.1044/1092‐4388(2009/07‐0144).Thurman, A. J., and C. B. Mervis. 2013. “The Regulatory Function of Social Referencing in Preschoolers With Down Syndrome or Williams Syndrome.” Journal of Neurodevelopmental Disorders 5, no. 1: 2. https://doi.org/10.1186/1866‐1955‐5‐2.Touchet, C., R. Pochon, B. Bourdin, and L. Ibernon. 2023. “Cognitive Profiles of French Individuals With Williams Syndrome and Down Syndrome: What's Up? What's Next?” Disabilities 3, no. 1: 105–113. https://doi.org/10.3390/disabilities3010008.Tricco, A. C., E. Lillie, W. Zarin, et al. 2018. “PRISMA Extension for Scoping Reviews (PRISMA‐ScR): Checklist and Explanation.” Annals of Internal Medicine 169, no. 7: 467–473. https://doi.org/10.7326/M18‐0850.Tsao, R., and C. Kindelberger. 2009. “Variability of Cognitive Development in Children With Down Syndrome: Relevance of Good Reasons for Using the Cluster Procedure.” Research in Developmental Disabilities 30, no. 3: 426–432. https://doi.org/10.1016/j.ridd.2008.10.009.van der Steen, S., Y. Hill, and R. F. A. Cox. 2023. “The Relationship Between Parent‐Child Movement Synchrony and Social Behavior of Children Diagnosed With Autism Spectrum Disorder and Children Diagnosed With Down Syndrome.” Journal of Developmental and Physical Disabilities 36: 843–863. https://doi.org/10.1007/s10882‐023‐09940‐6.Warnell, K. R., and E. Redcay. 2019. “Minimal Coherence Among Varied Theory of Mind Measures in Childhood and Adulthood.” Cognition 191: 103997. https://doi.org/10.1016/j.cognition.2019.06.009.Wellman, H. M., F. Fang, and C. C. Peterson. 2011. “Sequential Progressions in a Theory‐Of‐Mind Scale: Longitudinal Perspectives.” Child Development 82, no. 3: 780–792. https://doi.org/10.1111/j.1467‐8624.2011.01583.x.Wellman, H. M., and D. Liu. 2004. “Scaling of Theory‐Of‐Mind Tasks.” Child Development 75, no. 2: 523–541. https://doi.org/10.1111/j.1467‐8624.2004.00691.x.Wellman, H. M. 2014. Making Minds: How Theory of Mind Develops. Oxford University Press.Wishart, J. G. 2007. “Socio‐Cognitive Understanding: A Strength or Weakness in Down's Syndrome?” Journal of Intellectual Disability Research 51, no. 12: 996–1005. https://doi.org/10.1111/j.1365‐2788.2007.01007.x.Wong, P., and S. Leung. 2010. “The Development of Theory of Mind Ability in Autistic Children.” Psychologia 51: 151–162.4192467720260402202604022168-81841832026MarCureusCureusDigital Versus Conventional Splints in Patients With Temporomandibular Disorders: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.e105977e105977e10597710.7759/cureus.105977Occlusal splints are widely used as a conservative treatment modality for temporomandibular disorders (TMD). With the increasing integration of digital technologies in dentistry, computer-aided design and manufacturing (CAD/CAM) workflows have emerged. Although digital splints are increasingly adopted in clinical practice, their clinical effectiveness compared with conventionally fabricated splints remains uncertain. Electronic searches were performed in PubMed, Scopus, Web of Science (WOS), and Cochrane Central Register of Controlled Trials (CENTRAL) from database inception to March 2026 to identify randomized controlled trials (RCTs) comparing digital with conventional occlusal splints in patients with TMD. The primary outcome was the mean difference of pain intensity post intervention. Continuous outcomes were analyzed using mean differences (MDs) with 95% confidence intervals (CIs) using a random-effects model. Eight RCTs comprising 390 patients were included. Seven studies reported posttreatment pain scores, showing no significant difference between digital and conventional splints (MD = -0.22; 95% CI = -0.92 to 0.48; p = 0.54; I² = 77.9%). Similarly, no significant differences were observed in limited mouth opening or maximum mouth opening. Patient satisfaction was also comparable between groups. However, digitally fabricated splints were associated with significantly shorter clinical occlusal adjustment time compared with conventional splints (MD = -9.10 minutes; 95% CI = -15.18 to -3.02; p < 0.001). Digital and conventional occlusal splints provide comparable clinical outcomes in patients with TMD with respect to pain relief, mouth opening, and patient satisfaction. However, digitally fabricated splints may offer a practical advantage by reducing clinical adjustment time. Further high-quality trials with standardized outcomes and longer follow-up are warranted.Copyright © 2026, Almansour et al.AlmansourFahad WFWDepartment of Dentistry, Kuwait Ministry of Health, Kuwait City, KWT.AlshammariKhaledKDepartment of Dentistry, Kuwait Ministry of Health, Kuwait City, KWT.Mohammed AlhajriDhariDDepartment of Dentistry, Kuwait Ministry of Health, Kuwait City, KWT.Bader AlzamananMaryamMDepartment of Dentistry, Kuwait Ministry of Health, Kuwait City, KWT.Hamed AlduaijFayFDepartment of Dentistry, Kuwait Ministry of Health, Kuwait City, KWT.Walled AbdullahKhaledKDepartment of Dentistry, Kuwait Ministry of Health, Kuwait City, KWT.Homoud AlnoumasMohammadMDepartment of Dentistry, Kuwait Ministry of Health, Kuwait City, KWT.AbdelazizAhmedADepartment of Biostatistics, Faculty of Medicine, Al-Azhar University, Cairo, EGY.engJournal ArticleReview20260327United StatesCureus1015967372168-8184cad/camdigital dentistrydigital occlusal splintstemporomandibular joint (tmj) disorderstmdConflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.20263272026426312026426302026424462026327epublish41924677PMC1303756010.7759/cureus.105977Temporomandibular disorders: a review of current concepts in aetiology, diagnosis and management. Kapos FP, Exposto FG, Oyarzo JF, Durham J. Oral Surg. 2020;13:321–334.PMC863158134853604Associations between temporomandibular pain and biobehavioral variables in dental students in response to an external stressor. Somoskövi I, Radnai M, Ohrbach R, Dergez T, Tiringer I, Radácsi A, Nagy Á. J Oral Facial Pain Headache. 2023;37:167–176.PMC1066470437975781Global prevalence of temporomandibular disorders: a systematic review and meta-analysis. Alqutaibi AY, Alhammadi MS, Hamadallah HH, et al. J Oral Facial Pain Headache. 2025;39:48–65.PMC1253158041070533Temporomandibular disorders: rapid evidence review. Matheson EM, Fermo JD, Blackwelder RS. https://pubmed.ncbi.nlm.nih.gov/36689971/ Am Fam Physician. 2023;107:52–58.36689971Assessment of using occlusal splints without other adjunctive treatment modules in the management of temporomandibular disorders: a systematic review of literature. Chahrour M, Reda B. Cureus. 2025;17:0.PMC1243196040951096Occlusal splints-types and effectiveness in temporomandibular disorder management. Albagieh H, Alomran I, Binakresh A, et al. Saudi Dent J. 2023;35:70–79.PMC993150436817028Oral splint for temporomandibular joint disorders with revolutionary fluid system. Srivastava R, Jyoti B, Devi P. https://pmc.ncbi.nlm.nih.gov/articles/PMC3760352/ Dent Res J (Isfahan) 2013;10:307–313.PMC376035224019797Oral splints for patients with temporomandibular disorders or bruxism: a systematic review and economic evaluation. Riley P, Glenny AM, Worthington HV, et al. Health Technol Assess. 2020;24:1–224.PMC704990832065109Clinical application of CAD/CAM technology in 3D printed dental model used for removable splints fabrication in the treatment of traumatized anterior permanent teeth in children. Wang XT, Yang X, Guo HQ, et al. J Dent. 2024;151:105404.39401583Pilot clinical comparison of three occlusal splint fabrication techniques: a preliminary study. Torné-Durán S, Marco-Martínez L, Serrano-Belmonte I. Sci Rep. 2025;16:2963.PMC1283058141413207Comparative efficacy of digital 3D-printed and conventional stable occlusal splints in the treatment of temporomandibular disorders. Qin H, Liu Y, Miao H, et al. J Oral Rehabil. 2025;52:1699–1706.PMC1242646140384555Application of fully digital occlusal splints in painful temporomandibular disorders: a randomized controlled clinical study. Chen J, Yin W, Ding S, Cao J, Ding X, Liang Y, Xiao J. Head Face Med. 2026;22:16.PMC1290370141495778Assessment of 3D-printed occlusal splint used in the treatment of temporomandibular disorder in female patients. Abd Elgaleel AS, AlMahdy YF, Bahaa SM. Bull Stomatol Maxillofac Surg. 2025;21:305–315.Efficacy of digitally fabricated occlusal splints for treatment temporomandibular disorders: a randomized controlled trial. Hussein HA, Saied DA, Hassan DT. https://asdj.journals.ekb.eg/article_429043_78584c7db4bea1f12eedefe2bc40d8c2.pdf Ain Shams Dent J. 2025;37:409–418.The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. Liberati A, Altman DG, Tetzlaff J, et al. BMJ. 2009;339:0.PMC271467219622552Cochrane Handbook for Systematic Reviews of Interventions (current version) [
Feb;
2025
]. 2024. https://training.cochrane.org/handbook https://training.cochrane.org/handbookAssessment of chronic pain: domains, methods, and mechanisms. Fillingim RB, Loeser JD, Baron R, Edwards RR. J Pain. 2016;17:0–20.PMC501065227586827Digital versus conventional stabilization splints in pain-related temporomandibular disorders: a randomized controlled crossover evaluation of functional and patient-centered outcomes - a pilot study. Pai UY, Aras MA, Ahmed J, Natarajan S, Nagarsekar A, Sales A. J Indian Acad Oral Med Radiol. 2025;37:495–501.RoB 2: a revised tool for assessing risk of bias in randomised trials. Sterne JA, Savović J, Page MJ, et al. BMJ. 2019;366:0.31462531Patient’s satisfaction and muscles activity after management of temporomandibular disorders patients using computer-aided design/computer-aided manufacturing versus conventional occlusal splints (randomized clinical trial) Algabri RS, Alqutaibi AY, Elkadem AH, Maher EA, Kaddah AF. Int Dent Med J Adv Res. 2017;3:1–8.Clinical comparison of conventional and additive manufactured stabilization splints. Berntsen C, Kleven M, Heian M, Hjortsjö C. Acta Biomater Odontol Scand. 2018;4:81–89.PMC609501930128331Parallel randomized controlled clinical trial in patients with temporomandibular disorders treated with a CAD/CAM versus a conventional stabilization splint. Pho Duc JM, Hüning SV, Grossi ML. Int J Prosthodont. 2016;29:340–350.27479339Management of pain in patients with temporomandibular disorder (TMD): challenges and solutions. Gil-Martínez A, Paris-Alemany A, López-de-Uralde-Villanueva I, La Touche R. J Pain Res. 2018;11:571–587.PMC585991329588615Conservative and minimally invasive interventions for temporomandibular disorders: a systematic review and meta-analysis of randomized controlled trials. Iturbe Cordero AA, Jaramillo AP, Omana Bautista NM, Munoz Armijos KN, Cooley DE, Delgado Castromonte HE. Cureus. 2025;17:0.PMC1251616941089095Multimodal approaches in the management of temporomandibular disorders: a narrative review. Dąbkowska I, Sobiech L, Czępińska A, Bęben A, Turżańska K, Gawda P. J Clin Med. 2025;14:4326.PMC1219398840566071The effectiveness of occlusal splint therapy vs physiotherapy in the management of temporomandibular disorders: a prospective comparative study. Chafii A, El Bassity I, Ben El Hammi N, Ait Dambark A, Jouhadi EM. Cureus. 2026;18:0.PMC1287219441646618Effects of physiotherapy on pain and mouth opening in temporomandibular disorders: an umbrella and mapping systematic review with meta-meta-analysis. Arribas-Pascual M, Hernández-Hernández S, Jiménez-Arranz C, Grande-Alonso M, Angulo-Díaz-Parreño S, La Touche R, Paris-Alemany A. J Clin Med. 2023;12:788.PMC991769836769437Centric stabilization occlusal splints vs. other conservative therapies in the management of temporomandibular disorders: a systematic review and meta-analysis. Gupta AK, Singh RK, Narula V. Saudi Dent J. 2025;37:52.PMC1252863641091278Effectiveness of manual therapy and therapeutic exercise for temporomandibular disorders: systematic review and meta-analysis. Armijo-Olivo S, Pitance L, Singh V, Neto F, Thie N, Michelotti A. Phys Ther. 2016;96:9–25.PMC470659726294683Factors associated with patient-reported mouth opening activity limitations in individuals with persistent intra-articular temporomandibular disorders: a cross-sectional study exploring physical and self-reported outcomes. Dinsdale A, Thomas L, Forbes R, Treleaven J. Musculoskelet Sci Pract. 2024;73:103166.39167861Conservative versus invasive approaches in temporomandibular disc displacement: a systematic review of randomized controlled clinical trials. Sá M, Faria C, Pozza DH. Dent J (Basel) 2024;12:244.PMC1135376439195088Comparative effectiveness of digitally and conventionally fabricated occlusal splints with low-power laser therapy in treating temporomandibular disorders. Ghoveizi R, Habibi L, Beyabanaki E, Tavakolizadeh S, Akbari H. J Lasers Med Sci. 2025;16:0.PMC1295826041789290Quality communication can improve patient-centred health outcomes among older patients: a rapid review. Sharkiya SH. BMC Health Serv Res. 2023;23:886.PMC1046425537608376Revolutionizing dental restorations: insights into computer-aided design/computer-aided manufacturing materials - a systematic review. Ramnarayan BK, Nagral SM, Nanaiah P, Satelur KP, Venkatasubramanian R, Avinash J. Dent Res J (Isfahan) 2025;22:52.PMC1284455041607733Advantages and drawbacks of different methods to measure marginal gaps in fixed dental prostheses: a scoping review. Ayres AP, Cuschieri LA, Bianchi DM, Pradíes G, Côrtes AR. J Dent. 2024;151:105400.39393607Efficacy of appliance therapy on temporomandibular disorder related facial pain and mandibular mobility: a randomized controlled study. Katyayan PA, Katyayan MK, Shah RJ, Patel G. J Indian Prosthodont Soc. 2014;14:251–261.PMC414851225183909Recommendations on the use of oral orthotic occlusal appliance therapy for temporomandibular joint disorders: current evidence and clinical practice. Bhargava D, Chávez Farías C, Ardizone García I, et al. J Maxillofac Oral Surg. 2023;22:579–589.PMC1039043937534353Temporomandibular disorders: improving outcomes using a multidisciplinary approach. Garrigós-Pedrón M, Elizagaray-García I, Domínguez-Gordillo AA, Del-Castillo-Pardo-de-Vera JL, Gil-Martínez A. J Multidiscip Healthc. 2019;12:733–747.PMC673256531564890Orofacial pain and dentistry management: guidelines for a more comprehensive evidence-based approach. Labanca M, Gianò M, Franco C, Rezzani R. Diagnostics (Basel) 2023;13:2854.PMC1048662337685392Temporomandibular disorders: old ideas and new concepts. List T, Jensen RH. Cephalalgia. 2017;37:692–704.280687904191923120260401202604011663-9812172026Frontiers in pharmacologyFront PharmacolThe rise and evolution of cancer mechanobiology: a bibliometric trajectory of three decades of research.16997091699709169970910.3389/fphar.2026.1699709A growing body of research indicates that mechanobiology plays a pivotal role in cancer pathogenesis and holds considerable therapeutic potential. However, a comprehensive bibliometric analysis of this interdisciplinary field is lacking, partly due to challenges in cross-database data integration. In this study, we aim to construct a systematic knowledge map of cancer mechanobiology to delineate its research progress, core structure, and emerging trends.In this study, we integrated 1,947 publications from the Web of Science (WoS) Core Collection and Scopus (1976-2025). To address cross-database heterogeneity, we developed a novel, customized, multi-stage data-standardization workflow combining a bespoke Python parsing engine with fuzzy string matching algorithms and manual verification. The unified dataset was analyzed using CiteSpace, VOSviewer, and Bibliometrix.The United States and China are the most prolific countries, while the University of California system is the most productive institution. Valerie M. Weaver is the most published author, while Matthew J. Paszek is the most co-cited, indicating foundational influence. Cell is the most influential journal based on co-citation frequency. Keyword analysis reveals a thematic evolution from "extracellular matrix stiffness" and "mechanotransduction" to frontier areas such as "cancer immunotherapy" and "YAP signaling protein."In this study, we construct a comprehensive bibliometric map of cancer mechanobiology. Our findings elucidate the developmental trajectory and research hotspots of the field, providing a data-driven reference for future investigations, international collaborations, and clinical translation of physical oncology.Copyright © 2026 Liu, Liu, Wang, Han and Dang.LiuBoyanBNinth Hospital of Xi'an, Xi'an, China.Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China.LiuXufengXNinth Hospital of Xi'an, Xi'an, China.WangYueYShanxi Academy of Traditional Chinese Medicine, Xi'an, China.HanXiaoXNinth Hospital of Xi'an, Xi'an, China.DangXiyuXNinth Hospital of Xi'an, Xi'an, China.engJournal ArticleSystematic Review20260316SwitzerlandFront Pharmacol1015489231663-9812bibliometric analysis (BA)cancer immunotherapycancer mechanobiologyextracellular matrixknowledge maptumor microenvironmentThe author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.2025952026224202622820264175202641742026414222026316epublish41919231PMC1303373310.3389/fphar.2026.16997091699709Abdulhayoglu M. A., Thijs B. (2018). Use of locality sensitive hashing (LSH) algorithm to match web of science and scopus. Scientometrics 116 (2), 1229–1245. 10.1007/s11192-017-2569-610.1007/s11192-017-2569-6Aksnes D., Sivertsen G. (2019). A criteria-based assessment of the coverage of scopus and web of science. J. Data Inf. Sci. 4, 1–21. 10.2478/jdis-2019-000110.2478/jdis-2019-0001Angeli S., Neophytou C., Kalli M., Stylianopoulos T., Mpekris F. (2025). The mechanopathology of the tumor microenvironment: detection techniques, molecular mechanisms and therapeutic opportunities. Front. Cell Dev. Biol. 13, 1564626. 10.3389/fcell.2025.156462610.3389/fcell.2025.1564626PMC1195872040171226Bernards R., Jaffee E., Joyce J. A., Lowe S. W., Mardis E. R., Morrison S. J., et al. (2020). A roadmap for the next decade in cancer research. Nat. Cancer 1 (1), 12–17. 10.1038/s43018-019-0015-910.1038/s43018-019-0015-935121845Binnig G., Quate C. F., Gerber C. (1986). Atomic force microscope. Phys. Rev. Lett. 56 (9), 930–933. 10.1103/PhysRevLett.56.93010.1103/PhysRevLett.56.93010033323Bissell M. J., Hall H. G., Parry G. (1982). How does the extracellular matrix direct gene expression? J. Theor. Biol. 99 (1), 31–68. 10.1016/0022-5193(82)90388-510.1016/0022-5193(82)90388-56892044Bornmann L., Wagner C., Leydesdorff L. (2014). BRICS countries and scientific excellence: a bibliometric analysis of most frequently-cited papers. J. Am. Soc. Inf. Sci. Technol. 66, 1507–1513. 10.1002/asi.2333310.1002/asi.23333Cambi A., Ventre M. (2022). Collagen-based biomimetic systems to study the biophysical tumour microenvironment. Cancers (Basel) 14 (23), 5939. 10.3390/cancers1423593910.3390/cancers14235939PMC973981436497421Chaudhuri P. K., Low B. C., Lim C. T. (2018). Mechanobiology of tumor growth. Chem. Rev. 118 (14), 6499–6515. 10.1021/acs.chemrev.8b0004210.1021/acs.chemrev.8b0004229927236Chaudhuri O., Cooper-White J., Janmey P. A., Mooney D. J., Shenoy V. B. (2020). Effects of extracellular matrix viscoelasticity on cellular behaviour. Nature 584 (7822), 535–546. 10.1038/s41586-020-2612-210.1038/s41586-020-2612-2PMC767615232848221Chen C. (2006). CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J. Am. Soc. Inf. Sci. Technol. 57 (3), 359–377. 10.1002/asi.2031710.1002/asi.20317Chen M., Zhang Y., Zhou P., Liu X., Zhao H., Zhou X., et al. (2020). Substrate stiffness modulates bone marrow-derived macrophage polarization through NF-κB signaling pathway. Bioact. Mater 5 (4), 880–890. 10.1016/j.bioactmat.2020.05.00410.1016/j.bioactmat.2020.05.004PMC733247032637751Dai M., Yuan X., Sun C., Han R., Björkholm M., Kong F., et al. (2025). The ETS transcription factor GABPA inhibits bladder cancer aggressiveness by repressing extracellular matrix deposition and mechanotransduction signaling. Cell Death Dis. 16 (1), 618. 10.1038/s41419-025-07935-z10.1038/s41419-025-07935-zPMC1235482940813762Dallavalasa S., Beeraka N. M., Basavaraju C. G., Tulimilli S. V., Sadhu S. P., Rajesh K., et al. (2021). The role of tumor associated macrophages (TAMs) in cancer progression, chemoresistance, angiogenesis and metastasis - current status. Curr. Med. Chem. 28 (39), 8203–8236. 10.2174/092986732866621072014372110.2174/092986732866621072014372134303328de Visser K. E., Joyce J. A. (2023). The evolving tumor microenvironment: from cancer initiation to metastatic outgrowth. Cancer Cell 41 (3), 374–403. 10.1016/j.ccell.2023.02.01610.1016/j.ccell.2023.02.01636917948Dombroski J. A., Hope J. M., Sarna N. S., King M. R. (2021). Channeling the force: piezo1 mechanotransduction in cancer metastasis. Cells 10 (11), 2815. 10.3390/cells1011281510.3390/cells10112815PMC861647534831037Dong D.-L., Zhou Y.-Y., Jin G. Z. (2024a). Trends and hotspots in mechanobiology: a bibliometric analysis from 2010 to 2024. Preprints. 10.20944/preprints202411.0860.v110.20944/preprints202411.0860.v1Dong Y., Lu M., Yin Y., Wang C., Dai N. (2024b). Tumor biomechanics-inspired future medicine. Cancers (Basel) 16 (23), 4107. 10.3390/cancers1623410710.3390/cancers16234107PMC1164010039682291Dupont S., Wickström S. A. (2022). Mechanical regulation of chromatin and transcription. Nat. Rev. Genet. 23 (10), 624–643. 10.1038/s41576-022-00493-610.1038/s41576-022-00493-635606569Dupont S., Morsut L., Aragona M., Enzo E., Giulitti S., Cordenonsi M., et al. (2011). Role of YAP/TAZ in mechanotransduction. Nature 474 (7350), 179–183. 10.1038/nature1013710.1038/nature1013721654799Engler A. J., Sen S., Sweeney H. L., Discher D. E. (2006). Matrix elasticity directs stem cell lineage specification. Cell 126 (4), 677–689. 10.1016/j.cell.2006.06.04410.1016/j.cell.2006.06.04416923388Ezeobidi E. I., Truszkowska A. (2025). Modeling the dynamics of circulating tumor cell clusters inside a microfluidic channel. Biomicrofluidics 19 (1), 014103. 10.1063/5.024916510.1063/5.0249165PMC1182127339949346Finger A. M., Hendley A. M., Figueroa D., Gonzalez H., Weaver V. M. (2025). Tissue mechanics in tumor heterogeneity and aggression. Trends Cancer 11 (8), 806–824. 10.1016/j.trecan.2025.04.00410.1016/j.trecan.2025.04.004PMC1235007540307158Franceschini F., Maisano D., Mastrogiacomo L. (2016). Empirical analysis and classification of database errors in scopus and web of science. J. Inf. 10 (4), 933–953. 10.1016/j.joi.2016.07.00310.1016/j.joi.2016.07.003Fung Y. C. (2013). Biomechanics: motion, flow, stress, and growth. New York: Springer.Gajda A. M., Rodríguez-López R., Er E. E. (2025). Targeting cancer cell stiffness and metastasis with clinical therapeutics. Clin. Exp. Metastasis 42 (4), 34. 10.1007/s10585-025-10353-210.1007/s10585-025-10353-2PMC1215910540498131Graciotti M., Kandalaft L. E. (2025). Vaccines for cancer prevention: exploring opportunities and navigating challenges. Nat. Rev. Drug Discov. 24 (2), 134–150. 10.1038/s41573-024-01081-510.1038/s41573-024-01081-539622986Guo S. B., Liu D. Y., Fang X. J., Meng Y., Zhou Z. Z., Li J., et al. (2025a). Current concerns and future directions of large language model chatGPT in medicine: a machine-learning-driven global-scale bibliometric analysis. Int. J. Surg. 112, 2805–2822. 10.1097/js9.000000000000366810.1097/js9.000000000000366841133425Guo S. B., Liu D. Y., Hu R., Zhou Z. Z., Meng Y., Li H. L., et al. (2025b). Immune-related adverse events of neoadjuvant immunotherapy in patients with perioperative cancer: a machine-learning-driven, decade-long informatics investigation. J. Immunother. Cancer 13 (8), e011040. 10.1136/jitc-2024-01104010.1136/jitc-2024-011040PMC1237462740841132Harzing A.-W., Alakangas S. (2016). Google scholar, scopus and the web of science: a longitudinal and cross-disciplinary comparison. Scientometrics 106 (2), 787–804. 10.1007/s11192-015-1798-910.1007/s11192-015-1798-9Helmlinger G., Netti P. A., Lichtenbeld H. C., Melder R. J., Jain R. K. (1997). Solid stress inhibits the growth of multicellular tumor spheroids. Nat. Biotechnol. 15 (8), 778–783. 10.1038/nbt0897-77810.1038/nbt0897-7789255794Holmgren A., Edler D., Rosvall M. (2023). Mapping change in higher-order networks with multilevel and overlapping communities. Appl. Netw. Sci. 8 (1), 42. 10.1007/s41109-023-00572-510.1007/s41109-023-00572-5Holmvall K., Camper L., Johansson S., Kimura J. H., Lundgren-Akerlund E. (1995). Chondrocyte and chondrosarcoma cell integrins with affinity for collagen type II and their response to mechanical stress. Exp. Cell Res. 221 (2), 496–503. 10.1006/excr.1995.140110.1006/excr.1995.14017493650
Hong H. J., Lee N. H., Kim H. S., Leong K. W., Kim H. W. (2025). Biomechanics in miniature: microfluidic-based in vitro modeling to decipher mechanobiological phenomena. Trends Biotechnol. 44 (3), 678–695. 10.1016/j.tibtech.2025.07.024
10.1016/j.tibtech.2025.07.02440885668Huang W. B., Lai H. Z., Long J., Dai Z. L., Ma Q., Xiao C., et al. (2025). Biomechanics of the tumor extracellular matrix and regulatory T cells: regulatory mechanisms and potential therapeutic targets. Cell Commun. Signal 23 (1), 375. 10.1186/s12964-025-02380-z10.1186/s12964-025-02380-zPMC1236926940842015Jain R. K. (1987). Transport of molecules in the tumor interstitium: a review. Cancer Res. 47 (12), 3039–3051.3555767Jia H., Chen X., Zhang L., Chen M. (2025). Cancer associated fibroblasts in cancer development and therapy. J. Hematol. Oncol. 18 (1), 36. 10.1186/s13045-025-01688-010.1186/s13045-025-01688-0PMC1195419840156055Jiang Z. L. (2023). Mechanobiology research in China. Mechanobiol. Med. 1 (1), 100002. 10.1016/j.mbm.2023.10000210.1016/j.mbm.2023.100002PMC1208213640395869Jones A. F., Byrne H. M., Gibson J. S., Dold J. W. (2000). A mathematical model of the stress induced during avascular tumour growth. J. Math. Biol. 40 (6), 473–499. 10.1007/s00285000003310.1007/s00285000003310945645Kalli M., Poskus M. D., Stylianopoulos T., Zervantonakis I. K. (2023). Beyond matrix stiffness: targeting force-induced cancer drug resistance. Trends Cancer 9 (11), 937–954. 10.1016/j.trecan.2023.07.00610.1016/j.trecan.2023.07.006PMC1059242437558577Karalis T. T., Heldin P., Vynios D. H., Neill T., Buraschi S., Iozzo R. V., et al. (2019). Tumor-suppressive functions of 4-MU on breast cancer cells of different ER status: regulation of hyaluronan/HAS2/CD44 and specific matrix effectors. Matrix Biol. 78 (79), 118–138. 10.1016/j.matbio.2018.04.00710.1016/j.matbio.2018.04.00729673760Khanmohammadi M., Danish H., Sekar N. C., Suarez S. A., Chheang C., Peter K., et al. (2024). Cyclic stretch enhances neutrophil extracellular trap formation. BMC Biol. 22 (1), 209. 10.1186/s12915-024-02009-610.1186/s12915-024-02009-6PMC1140980439289752Ladoux B., Mège R. M. (2017). Mechanobiology of collective cell behaviours. Nat. Rev. Mol. Cell Biol. 18 (12), 743–757. 10.1038/nrm.2017.9810.1038/nrm.2017.9829115298
Lei K., Kurum A., Kaynak M., Bonati L., Han Y., Cencen V., et al. (2021). Cancer-cell stiffening via cholesterol depletion enhances adoptive T-cell immunotherapy. Nat. Biomed. Eng.
5 (12), 1411–1425. 10.1038/s41551-021-00826-6
10.1038/s41551-021-00826-6PMC761210834873307Lekka M., Laidler P., Gil D., Lekki J., Stachura Z., Hrynkiewicz A. Z. (1999). Elasticity of normal and cancerous human bladder cells studied by scanning force microscopy. Eur. Biophys. J. 28 (4), 312–316. 10.1007/s00249005021310.1007/s00249005021310394623Levental K. R., Yu H., Kass L., Lakins J. N., Egeblad M., Erler J. T., et al. (2009). Matrix crosslinking forces tumor progression by enhancing integrin signaling. Cell 139 (5), 891–906. 10.1016/j.cell.2009.10.02710.1016/j.cell.2009.10.027PMC278800419931152Li M. (2024). Harnessing atomic force microscopy-based single-cell analysis to advance physical oncology. Microsc. Res. Tech. 87 (4), 631–659. 10.1002/jemt.2446710.1002/jemt.2446738053519Linke J. A., Munn L. L., Jain R. K. (2024). Compressive stresses in cancer: characterization and implications for tumour progression and treatment. Nat. Rev. Cancer 24 (11), 768–791. 10.1038/s41568-024-00745-z10.1038/s41568-024-00745-zPMC1296732439390249Liu Q., Luo Q., Ju Y., Song G. (2020). Role of the mechanical microenvironment in cancer development and progression. Cancer Biol. Med. 17 (2), 282–292. 10.20892/j.issn.2095-3941.2019.043710.20892/j.issn.2095-3941.2019.0437PMC730946232587769Luo Z., Yao X., Li M., Fang D., Fei Y., Cheng Z., et al. (2022). Modulating tumor physical microenvironment for fueling CAR-T cell therapy. Adv. Drug Deliv. Rev. 185, 114301. 10.1016/j.addr.2022.11430110.1016/j.addr.2022.11430135439570Mai Z., Lin Y., Lin P., Zhao X., Cui L. (2024). Modulating extracellular matrix stiffness: a strategic approach to boost cancer immunotherapy. Cell Death Dis. 15 (5), 307. 10.1038/s41419-024-06697-410.1038/s41419-024-06697-4PMC1106321538693104Marginson S., Xu X. (2023). Hegemony and inequality in global science: problems of the center-periphery model. Comp. Educ. Rev. 67 (1), 31–52. 10.1086/72276010.1086/722760Meyer T., Castelein J., Schattenfroh J., Sophie Morr A., Vieira da Silva R., Tzschätzsch H., et al. (2024). Magnetic resonance elastography in a nutshell: tomographic imaging of soft tissue viscoelasticity for detecting and staging disease with a focus on inflammation. Prog. Nucl. Magn. Reson Spectrosc. 144-145, 1–14. 10.1016/j.pnmrs.2024.05.00210.1016/j.pnmrs.2024.05.00239645347Miranda A., Gómez-Varela A. I., Stylianou A., Hirvonen L. M., Sánchez H., De Beule P. A. A. (2021). How did correlative atomic force microscopy and super-resolution microscopy evolve in the quest for unravelling enigmas in biology? Nanoscale 13 (4), 2082–2099. 10.1039/d0nr07203f10.1039/d0nr07203f33346312Mongeon P., Paul-Hus A. (2016). The journal coverage of web of science and scopus: a comparative analysis. Scientometrics 106 (1), 213–228. 10.1007/s11192-015-1765-510.1007/s11192-015-1765-5Mukherjee S., Warden E. A., Zhang J. (2025). YAP/TAZ: an epitome of tumorigenesis. Cancer Lett. 625, 217806. 10.1016/j.canlet.2025.21780610.1016/j.canlet.2025.217806PMC1244349240381686Muppala S., Xiao R., Krukovets I., Verbovetsky D., Yendamuri R., Habib N., et al. (2017). Thrombospondin-4 mediates TGF-β-induced angiogenesis. Oncogene 36 (36), 5189–5198. 10.1038/onc.2017.14010.1038/onc.2017.140PMC558949428481870Navarro G., Gómez-Autet M., Morales P., Rebassa J. B., Llinas Del Torrent C., Jagerovic N., et al. (2024). Homodimerization of CB(2) cannabinoid receptor triggered by a bivalent ligand enhances cellular signaling. Pharmacol. Res. 208, 107363. 10.1016/j.phrs.2024.10736310.1016/j.phrs.2024.10736339179054Nelson C. M., Xiao B., Wickström S. A., Dufrêne Y. F., Cosgrove D. J., Heisenberg C. P., et al. (2024). Mechanobiology: shaping the future of cellular form and function. Cell 187 (11), 2652–2656. 10.1016/j.cell.2024.04.00610.1016/j.cell.2024.04.00638788688Nia H. T., Munn L. L., Jain R. K. (2020). Physical traits of cancer. Science 370 (6516), eaaz0868. 10.1126/science.aaz086810.1126/science.aaz0868PMC827437833122355Nikolić D., Ivanović D., Ivanović L. (2024). An open-source tool for merging data from multiple citation databases. Scientometrics 129 (7), 4573–4595. 10.1007/s11192-024-05076-210.1007/s11192-024-05076-2Ninkov A., Frank J. R., Maggio L. A. (2022). Bibliometrics: methods for studying academic publishing. Perspect. Med. Educ. 11 (3), 173–176. 10.1007/s40037-021-00695-410.1007/s40037-021-00695-4PMC924016034914027Oliver T., Dembo M., Jacobson K. (1995). Traction forces in locomoting cells. Cell Motil. Cytoskelet. 31 (3), 225–240. 10.1002/cm.97031030610.1002/cm.9703103067585992Park Y., Kang D. H., Chung C. (2025). Integrating tumor macroenvironment, microenvironment and mechanobiology with organoid and organ-on-a-chip models for lung cancer immunotherapy. Lung Cancer 207, 108726. 10.1016/j.lungcan.2025.10872610.1016/j.lungcan.2025.10872640865371Paszek M. J., Weaver V. M. (2004). The tension mounts: mechanics meets morphogenesis and malignancy. J. Mammary Gland. Biol. Neoplasia 9 (4), 325–342. 10.1007/s10911-004-1404-x10.1007/s10911-004-1404-x15838603Paszek M. J., Zahir N., Johnson K. R., Lakins J. N., Rozenberg G. I., Gefen A., et al. (2005). Tensional homeostasis and the malignant phenotype. Cancer Cell 8 (3), 241–254. 10.1016/j.ccr.2005.08.01010.1016/j.ccr.2005.08.01016169468Pedersen S., Pedersen S. F., Nilius B., Lambert I. H., Hoffmann E. K. (1999). Mechanical stress induces release of ATP from ehrlich ascites tumor cells. Biochimica Biophysica Acta (BBA) - Biomembr. 1416 (1), 271–284. 10.1016/S0005-2736(98)00228-410.1016/S0005-2736(98)00228-49889382Peng H., Chao Z., Wang Z., Hao X., Xi Z., Ma S., et al. (2025). Biomechanics in the tumor microenvironment: from biological functions to potential clinical applications. Exp. Hematol. Oncol. 14 (1), 4. 10.1186/s40164-024-00591-710.1186/s40164-024-00591-7PMC1172450039799341Peranzoni E., Rivas-Caicedo A., Bougherara H., Salmon H., Donnadieu E. (2013). Positive and negative influence of the matrix architecture on antitumor immune surveillance. Cell Mol. Life Sci. 70 (23), 4431–4448. 10.1007/s00018-013-1339-810.1007/s00018-013-1339-8PMC1111338223649148Pontes B., Mendes F. A. (2023). Mechanical properties of glioblastoma: perspectives for YAP/TAZ signaling pathway and beyond. Diseases 11 (2), 86. 10.3390/diseases1102008610.3390/diseases11020086PMC1029683037366874Pranckute R. (2021). Web of science (WoS) and scopus: the titans of bibliographic information in today’s academic world. Publ. 9, 12. 10.3390/publications901001210.3390/publications9010012Qiu Y., Gao T., Smith B. R. (2024). Mechanical deformation and death of circulating tumor cells in the bloodstream. Cancer Metastasis Rev. 43 (4), 1489–1510. 10.1007/s10555-024-10198-310.1007/s10555-024-10198-3PMC1190089838980581Qu P., Zhang H. (2025). The dual role of Piezo1 in tumor cells and immune cells: a new target for cancer therapy. Front. Immunol. 16, 1635388. 10.3389/fimmu.2025.163538810.3389/fimmu.2025.1635388PMC1235040140821847Rao J., Lim C. T., Hu T., Han D. (2021). Editorial: cancer cell mechanobiology - a new frontier for cancer invasion and metastasis research. Front. Cell Dev. Biol. 9, 775012. 10.3389/fcell.2021.77501210.3389/fcell.2021.775012PMC852921434692713
Rauner G., Gupta P. B., Kuperwasser C. (2025). From 2D to 3D and beyond: the evolution and impact of in vitro tumor models in cancer research. Nat. Methods
22, 1776–1787. 10.1038/s41592-025-02769-1
10.1038/s41592-025-02769-140715728Ren T., Sun L., Zheng Y., Jiang Y., Guo Y., Ma J. (2025). Mechanical forces and immune cells in the tumor microenvironment: from regulation mechanisms to therapeutic strategies. Int. J. Surg. 111 (8), 5420–5434. 10.1097/js9.000000000000263610.1097/js9.000000000000263640478969Rodrigues D. B., Reis R. L., Pirraco R. P. (2024). Modelling the complex nature of the tumor microenvironment: 3D tumor spheroids as an evolving tool. J. Biomed. Sci. 31 (1), 13. 10.1186/s12929-024-00997-910.1186/s12929-024-00997-9PMC1080449038254117Roerden M., Spranger S. (2025). Cancer immune evasion, immunoediting and intratumour heterogeneity. Nat. Rev. Immunol. 25 (5), 353–369. 10.1038/s41577-024-01111-810.1038/s41577-024-01111-839748116Roy Choudhury A., Gupta S., Chaturvedi P. K., Kumar N., Pandey D. (2019). Mechanobiology of cancer stem cells and their niche. Cancer Microenviron. 12 (1), 17–27. 10.1007/s12307-019-00222-410.1007/s12307-019-00222-4PMC652950031004332Sacks D., Baxter B., Campbell B. C. V., Carpenter J. S., Cognard C., Dippel D., et al. (2018). Multisociety consensus quality improvement revised consensus statement for endovascular therapy of acute ischemic stroke. Int. J. Stroke 13 (6), 612–632. 10.1177/174749301877871310.1177/174749301877871329786478Sánchez A. D., de la Cruz Del Río Rama M., García J. Á. (2017). Bibliometric analysis of publications on wine tourism in the databases scopus and WoS. Eur. Res. Manag. Bus. Econ. 23 (1), 8–15. 10.1016/j.iedeen.2016.02.00110.1016/j.iedeen.2016.02.001Santos A., Lagares D. (2018). Matrix stiffness: the conductor of organ fibrosis. Curr. Rheumatol. Rep. 20 (1), 2. 10.1007/s11926-018-0710-z10.1007/s11926-018-0710-z29349703Shen T., Wang Y., Cheng L., Bode A. M., Gao Y., Zhang S., et al. (2025). Oxidative complexity: the role of ROS in the tumor environment and therapeutic implications. Bioorg Med. Chem. 127, 118241. 10.1016/j.bmc.2025.11824110.1016/j.bmc.2025.11824140383035Sleeboom J. J. F., van Tienderen G. S., Schenke-Layland K., van der Laan L. J. W., Khalil A. A., Verstegen M. M. A. (2024). The extracellular matrix as hallmark of cancer and metastasis: from biomechanics to therapeutic targets. Sci. Transl. Med. 16 (728), eadg3840. 10.1126/scitranslmed.adg384010.1126/scitranslmed.adg384038170791Suffredini G., Gao W. D., Dodd O. J. (2024). Ultrasound shear wave elastography evaluation of the liver and implications for perioperative medicine. J. Clin. Med. 13 (13), 3633. 10.3390/jcm1313363310.3390/jcm13133633PMC1124219238999199
Tang R., Zhang Z., Liu X., Liao Y., Chen Y., Xiao M., et al. (2025). Stromal stiffness-regulated IGF2BP2 in pancreatic cancer drives immune evasion via sphingomyelin metabolism. Gastroenterology
169 (4), 615–631.e632. 10.1053/j.gastro.2025.03.019
10.1053/j.gastro.2025.03.01940158738Thompson D. F., Walker C. K. (2015). A descriptive and historical review of bibliometrics with applications to medical sciences. Pharmacotherapy 35 (6), 551–559. 10.1002/phar.158610.1002/phar.158625940769Urcun S., Lorenzo G., Baroli D., Rohan P.-Y., Sciumè G., Skalli W., et al. (2022). “Chapter six - oncology and mechanics: landmark studies and promising clinical applications,” in Advances in applied mechanics. Editor Bordas S. P. A. (Elsevier; ), 513–571.Vera-Baceta M. A., Thelwall M., Kousha K. (2019). Web of science and scopus language coverage. Scientometrics 121 (3), 1803–1813. 10.1007/s11192-019-03264-z10.1007/s11192-019-03264-zWang C., Jesiek B., Zhang W. (2024a). Elevating international collaboration and academic outcomes through strategic research funding: a bibliometric analysis of China scholarship council funded publications. Scientometrics 129 (7), 4329–4351. 10.1007/s11192-024-05054-810.1007/s11192-024-05054-8Wang R., Huang S., Wang P., Shi X., Li S., Ye Y., et al. (2024b). Bibliometric analysis of the application of deep learning in cancer from 2015 to 2023. Cancer Imaging 24 (1), 85. 10.1186/s40644-024-00737-010.1186/s40644-024-00737-0PMC1122342038965599Wang H., Zhou F., Qin W., Yang Y., Li X., Liu R. (2025a). Metabolic regulation of myeloid-derived suppressor cells in tumor immune microenvironment: targets and therapeutic strategies. Theranostics 15 (6), 2159–2184. 10.7150/thno.10527610.7150/thno.105276PMC1184073139990210Wang Y., Zhou H., Ju S., Dong X., Zheng C. (2025b). The solid tumor microenvironment and related targeting strategies: a concise review. Front. Immunol. 16, 1563858. 10.3389/fimmu.2025.156385810.3389/fimmu.2025.1563858PMC1197913140207238
Weaver V. M., Petersen O. W., Wang F., Larabell C. A., Briand P., Damsky C., et al. (1997). Reversion of the malignant phenotype of human breast cells in three-dimensional culture and in vivo by integrin blocking antibodies. J. Cell Biol.
137 (1), 231–245. 10.1083/jcb.137.1.231
10.1083/jcb.137.1.231PMC21398589105051Winkler J., Abisoye-Ogunniyan A., Metcalf K. J., Werb Z. (2020). Concepts of extracellular matrix remodelling in tumour progression and metastasis. Nat. Commun. 11 (1), 5120. 10.1038/s41467-020-18794-x10.1038/s41467-020-18794-xPMC754770833037194Wu X., Fei W., Shen T., Ye L., Li C., Chu S., et al. (2025). Unveiling the potential of biomechanics in pioneering innovative strategies for cancer therapy. Theranostics 15 (7), 2903–2932. 10.7150/thno.10860510.7150/thno.108605PMC1189830040083943Xin Y., Li K., Huang M., Liang C., Siemann D., Wu L., et al. (2023). Biophysics in tumor growth and progression: from single mechano-sensitive molecules to mechanomedicine. Oncogene 42 (47), 3457–3490. 10.1038/s41388-023-02844-x10.1038/s41388-023-02844-xPMC1065629037864030Xing H., Liu H., Chang Z., Zhang J. (2024). Research progress on the immunological functions of Piezo1 a receptor molecule that responds to mechanical force. Int. Immunopharmacol. 139, 112684. 10.1016/j.intimp.2024.11268410.1016/j.intimp.2024.11268439008939Yu W., Sharma S., Rao E., Rowat A. C., Gimzewski J. K., Han D., et al. (2022). Cancer cell mechanobiology: a new frontier for cancer research. J. Natl. Cancer Cent. 2 (1), 10–17. 10.1016/j.jncc.2021.11.00710.1016/j.jncc.2021.11.007PMC1125661739035217Yuan Z., Li Y., Zhang S., Wang X., Dou H., Yu X., et al. (2023). Extracellular matrix remodeling in tumor progression and immune escape: from mechanisms to treatments. Mol. Cancer 22 (1), 48. 10.1186/s12943-023-01744-810.1186/s12943-023-01744-8PMC1000785836906534Zhang M., Zhang B. (2025). Extracellular matrix stiffness: mechanisms in tumor progression and therapeutic potential in cancer. Exp. Hematol. Oncol. 14 (1), 54. 10.1186/s40164-025-00647-210.1186/s40164-025-00647-2PMC1198426440211368Zhang X., Al-Danakh A., Zhu X., Feng D., Yang L., Wu H., et al. (2025a). Insights into the mechanisms, regulation, and therapeutic implications of extracellular matrix stiffness in cancer. Bioeng. Transl. Med. 10 (1), e10698. 10.1002/btm2.1069810.1002/btm2.10698PMC1171121839801760Zhang Y., Fu Q., Sun W., Yue Q., He P., Niu D., et al. (2025b). Mechanical forces in the tumor microenvironment: roles, pathways, and therapeutic approaches. J. Transl. Med. 23 (1), 313. 10.1186/s12967-025-06306-810.1186/s12967-025-06306-8PMC1189983140075523Zhu P., Lu H., Wang M., Chen K., Chen Z., Yang L. (2023). Targeted mechanical forces enhance the effects of tumor immunotherapy by regulating immune cells in the tumor microenvironment. Cancer Biol. Med. 20 (1), 44–55. 10.20892/j.issn.2095-3941.2022.049110.20892/j.issn.2095-3941.2022.0491PMC98434463664777941913076202603301365-27022026Mar30Journal of clinical nursingJ Clin NursRecommendations for Arterial Blood Gas Collection in Intensive Care: Scoping Review.10.1111/jocn.70312To map and synthesise the main recommendations for arterial blood gas (ABG) collection in intensive care units (ICUs).A scoping review was conducted according to the PRISMA-ScR Checklist, supported with The PAGER framework and guided by the Joanna Briggs Institute methodology to ensure methodological rigour and analytical comprehensiveness.Data collection was conducted from February to April 2024. The data sources included: Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, PubMed Central, Scientific Electronic Library Online (SciELO), Web of Science (WoS), SCOPUS, Science Direct, Virtual Health Library (VHL), Excerpta Medica database (Embase), CAPES Thesis and Dissertation Catalogue, Brazilian Digital Library of Theses and Dissertations (BDTD), Scientific Open Access Repository of Portugal (RCAAP), Theses Canada and the Oswaldo Cruz Foundation (Fiocruz) repository.Key findings recommend the radial artery as the preferred puncture site, an insertion angle of 30° to 45°, the use of 1 or 3 mL syringes and 20G or 23G gauge needles. Transport and storage at room temperature are advised. Cryoanalgesia and subcutaneous analgesia methods were found to be effective for pain management.The review highlights the best practices for arterial blood gas collection in critical care. The synthesized evidence strengthens clinical practice, informs guidelines for intensive care nursing and promotes safer, higher-quality care for critically ill patients.The evidence-based recommendations identified can enhance nursing care related to arterial blood gas collection. Adherence to these practices promotes safer, more humanised and evidence-based care during the procedure.The report of this study followed the PRISMA-ScR Checklist.There was no patient or public involvement in this scoping review.© 2026 The Author(s). Journal of Clinical Nursing published by John Wiley & Sons Ltd.SouzaLaura LimaLL0000-0002-4976-1543Federal University of Rio Grande do Norte, Natal, Brazil.de LimaVanuza RaquelVR0000-0002-7785-0260Federal University of Rio Grande do Norte, Natal, Brazil.da CostaLuzia Clênia CamposLCC0009-0009-4608-7221Federal University of Rio Grande do Norte, Natal, Brazil.SoaresSamantha GuerreroSG0009-0007-3102-205XFederal University of Rio Grande do Norte, Natal, Brazil.Dos Santos PaivaFernanda MirellyFM0000-0002-6278-6470Federal University of Rio Grande do Norte, Natal, Brazil.de Oliveira SilvaSilmaraS0000-0002-2554-5045Federal University of Rio Grande do Norte, Natal, Brazil.DinizKéssya DantasKD0000-0001-6784-3166Federal University of Rio Grande do Norte, Natal, Brazil.Neves DantasRodrigo AssisRA0000-0002-9309-2092Federal University of Rio Grande do Norte, Natal, Brazil.RibeiroKátia Regina BarrosKRB0000-0002-3513-7483Federal University of Rio Grande do Norte, Natal, Brazil.engJournal ArticleReview20260330EnglandJ Clin Nurs92073020962-1067IMadultblood gas analysisintensive care unitsnursing20251014202543020263172026331053202633105320263302343aheadofprint4191307610.1111/jocn.70312ReferencesAksun, S., B. Uyan, M. Aksun, et al. 2018. “Comparison of Blood Gas Measurements Drawn With Synchronous Dried and Liquid Heparinized Injector and Biochemical Analyses.” Journal of Cardiovascular‐Thoracic Anaesthesia and Intensive Care Society 24, no. 3: 118–123. https://doi.org/10.5222/gkdad.2018.98704.American Association of Nurse Practitioners. 2024. “State Practice Environment.” https://www.aanp.org/advocacy/state/state‐practice‐environment.Australian College of Nurse Practitioners. 2023. “Standards of Practice for Nurse Practitioners in Australia.” https://www.acnp.org.au.Azevedo, R. S. 2021. “The Role of Nursing in UETI.” Scientific Journal—Faculdade Unimed 2, no. 3: 12–17.Batatais (SP). Municipal Health Department. 2024. “Arterial Collection: Standard Operating Procedure.” https://saude.batatais.sp.gov.br/wp‐content/uploads/2024/06/Coleta‐arterial.pdf.Borabadi, R., M. Rad, M. Rakhshani, and M. Rad. 2020. “Comparison of the Effects of Heparin 1000 and 5000 Units on Arterial Blood Gases.” Acta Facultatis Medicae Naissensis 37, no. 3: 267–273. https://doi.org/10.5937/afmnai2003267b.Bradbury‐Jones, C., H. Aveyard, O. R. Herber, L. Isham, J. Taylor, and L. O'Malley. 2021. “Scoping Reviews: The PAGER Framework for Improving Reporting Quality.” International Journal of Social Research Methodology: 1–14. https://doi.org/10.1080/13645579.2021.1899596.Brazil. Ministry of Health. 2013. Ordinance No. 529, of April 1, 2013. Official Gazette of the Union. https://bit.ly/2htwq8y.Canadian Nurses Association. 2019. “Advanced Practice Nursing: A Pan‐Canadian Framework.” https://www.cna‐aiic.ca/‐/media/cna/page‐content/pdf‐en/apn‐a‐pan‐canadian‐framework.pdf.Chandran, J., C. D'Silva, S. Sriram, and B. Krishna. 2021. “Clinical Utility of Arterial Blood Gas Test in an Intensive Care Unit: An Observational Study.” Indian Journal of Critical Care Medicine 25, no. 2: 172–175. https://doi.org/10.5005/jp‐journals‐10071‐23719.Federal Council of Nursing of Brazil. 2021. “Resolution No. 679/2021.” https://www.cofen.gov.br/resolucao‐cofen‐no‐679‐2021/.Freitas, M. A. D. S., J. L. de Melo, F. C. R. Pinto, et al. 2020. “Analytical Principles of Arterial Blood Gas Analysis.” Brazilian Journal of Clinical Analysis 52, no. 4: 318–321. https://doi.org/10.21877/2448‐3877.202100898.Haynes, J. M. 2014. “Randomized Controlled Trial of Cryoanalgesia (Ice Bag) to Reduce Pain Associated With Arterial Puncture.” Respiratory Care 60, no. 1: 1–5. https://doi.org/10.4187/respcare.03312.International Council of Nurses. 2020. “Guidelines on Advanced Practice Nursing.” https://www.icn.ch/system/files/documents/2020‐04/ICN_APN_Report_EN_WEB.pdf.Jensen, P. R., and B. A. Markewitz. 2016. “Safe Reduction of Blood Volume in the Blood Gas Laboratory.” Laboratory Medicine 47, no. 4: 326–329. https://doi.org/10.1093/labmed/lmw039.Kieninger, M., N. Zech, Y. Mulzer, et al. 2015. “Optimization of Blood Gas Analysis in the Intensive Care Unit.” Der Anaesthesist 64, no. 5: 365–372. https://doi.org/10.1007/s00101‐015‐0024‐8.Kucuk, A., H. H. Yuce, F. Yalcin, F. N. Boyacı, S. Yıldız, and S. Yalcin. 2014. “A 45‐Degree Wrist Angulation Is Ideal for Ultrasound‐Guided Long‐Axis Radial Artery Cannulation in Patients Over 60 Years Old: A Randomized Study.” Journal of Clinical Monitoring and Computing 28: 567–572.Marco, A. V. V., C. M. Castillo, and L. M. Soler. 2008. “Local Anesthesia in Arterial Puncture: Knowledge and Attitudes of Nurses.” Archivos de Bronconeumología 44, no. 7: 360–363.Matthews, M. N. 2020. “Accuracy of Arterial Blood Gas: Should Plastic Syringes Be Transported at Room Temperature or on Ice?” Canadian Journal of Medical Laboratory Science. https://search.ebscohost.com/login.aspx?direct=true.Ogliari, A. L. C., G. R. Piazzetta, and C. G. Martins Filho. 2021. “Arterial Puncture.” Vittalle—Journal of Health Sciences 33, no. 1: 124–131. https://doi.org/10.14295/vittalle.v33i1.11498.Pagnucci, N., S. Pagliaro, C. Maccheroni, M. Sichi, M. Scateni, and A. Tolotti. 2020. “Reducing Pain During Emergency Arterial Sampling Using Three Anesthetic Methods: A Randomized Controlled Clinical Trial.” Journal of Emergency Medicine 58, no. 6: 857–863. https://doi.org/10.1016/j.jemermed.2020.03.027.Patout, M., B. Lamia, E. Lhuillier, et al. 2015. “A Randomized Controlled Clinical Trial on the Effect of Needle Gauge on Pain and Anxiety Experienced During Radial Artery Puncture.” PLoS One 10, no. 9: e0139432. https://doi.org/10.1371/journal.pone.0139432.Peters, M. D. J., C. Marnie, A. C. Tricco, et al. 2020. “Updated Methodological Guidance for Conducting Scoping Reviews.” JBI Evidence Synthesis 18, no. 10: 2119–2126. https://doi.org/10.11124/JBIES‐20‐00167.PRISMA. 2020. “Transparent Reporting of Systematic Reviews and Meta‐Analyses.” https://www.prisma‐statement.org/.Rahim, S. N. A., N. Nordin, H. A. Zulkeflee, et al. 2025. “The Pre‐Analytical Aspects of Blood Gas Analysis Point‐Of‐Care‐Testing (POCT) and Their Impact on Clinical Decision‐Making.” Clin Lab 75, no. 5. https://www.10.7754/Clin.Lab.2024.241029.Rolim, L. R., E. M. Melo, N. M. Frota, N. G. Almeida, I. V. Barbosa, and J. Á. Caetano. 2013. “Knowledge of Intensive Care Unit Nursing Professionals About Arterial Blood Gas Analysis.” Journal of Nursing UFPE Online 7, no. 3: 713–721.Royal College of Nursing. 2022. “Advanced Level Nursing Practice: Introduction.” https://www.rcn.org.uk/professional‐development/publications/pub‐006895.Theodore, A. C. 2023. “Arterial Blood Gases.” https://www.uptodate.com/contents/arterial‐blood‐gases.Tricco, A. C., E. Lillie, W. Zarin, et al. 2018. “PRISMA Extension for Scoping Reviews (PRISMA‐ScR): Checklist and Explanation.” Annals of Internal Medicine 169, no. 7: 467–473. https://doi.org/10.7326/M18‐0850.Wade, R. G., J. Crawfurd, D. Wade, and R. Holland. 2015. “Radial Artery Blood Gas Sampling: A Randomized Controlled Trial of Lidocaine Local Anesthesia.” Journal of Evidence‐Based Medicine 8, no. 4: 185–191. https://doi.org/10.1111/jebm.12177.Watts, F. A., A. Ordoñez, A. González, et al. 2021. “Complications Associated With the Use of Radial Arterial Catheters in Relation to Their Length: Does Size Matter?” Journal of Vascular Access 24, no. 3: 483–486. https://doi.org/10.1177/11297298211034955.Woolley, A., and K. Hickling. 2003. “Errors in Blood Gas Measurement in the Intensive Care Unit: Effect of Delay in Estimation.” Journal of Critical Care 18, no. 1: 31–37.4191090220260330202604022196-10422712026Mar30Progress in orthodonticsProg OrthodPredicting transversal dental arch expansion outcomes with Invisalign aligners in permanent dentition: a systematic review and meta-analysis.1410.1186/s40510-026-00618-1To evaluate the predictability of Invisalign in producing transverse dental arch expansion in permanent dentition and to identify clinical and methodological factors influencing expansion accuracy.A systematic search of PubMed, Embase, Scopus, and WOS (up to November 2025) identified clinical studies reporting planned and achieved expansion. Predictability was defined as the achieved-to-planned expansion ratio and synthesized as logit-transformed proportions in random-effects meta-analyses. Tooth-group-specific pooled estimates were calculated for each arch. Meta-regressions, non-parametric correlations, linear and multivariable regressions, and hierarchical clustering explored the influence of publication year, age, arch, tooth type, material, aligner-change interval, and planned/achieved expansion. Risk of bias was assessed with ROBINS-I and certainty of evidence with GRADE.Twenty-three studies (1391 patients) were included; 21 entered the meta-analyses. Average predictability was 75.5% in the maxilla and 80.6% in the mandible. Mandibular second premolars showed the highest pooled predictability (88%), whereas lower second molars were least reliable (73%). In subsets reporting both levels, gingival-margin predictability was consistently lower than cusp-level values. Prediction intervals were very wide in both arches. Meta-regressions showed small but significant reductions in predictability in more recent publications (p = 0.01) and with increasing patient age (p = 0.016); greater achieved expansion showed only a modest positive association with predictability. Multivariable regression identified younger age (p = 0.001), SmartTrack material (p < 0.001), and mandibular segments (p = 0.001) as independent predictors of higher predictability. Publication bias was evident, and overall certainty of evidence was rated very low.Invisalign aligners provide moderate-to-high predictability for dentoalveolar transverse expansion in permanent dentition, particularly in premolar regions and in the mandibular arch. Expansion is mainly expressed as buccal crown tipping. Age, aligner material, and arch location modestly influence outcomes, underscoring the need for biologically realistic planning, selective overcorrections, and careful case selection.© 2026. The Author(s).SerafinMarcoMDepartment of Biomedical, Surgical, and Dental Sciences, University of Milan, Milan, Italy. marco.serafin@unimi.it.BoccalariElisaEDepartment of Biomedical, Surgical, and Dental Sciences, University of Milan, Milan, Italy.CalòMartaMDepartment of Biomedical, Surgical, and Dental Sciences, University of Milan, Milan, Italy.ZeccaPiero AntonioPADepartment of Medicine and Technological Innovation, University of Insubria, Varese, Italy.CaprioglioAlbertoADepartment of Biomedical, Surgical, and Dental Sciences, University of Milan, Milan, Italy.Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.engJournal ArticleSystematic ReviewMeta-AnalysisReview20260330GermanyProg Orthod1009363531723-7785IMHumansDental ArchDentition, PermanentPalatal Expansion TechniqueOrthodontic Appliances, RemovableTooth Movement TechniquesinstrumentationClear alignersExpansionMeta-analysisPredictabilitySystematic reviewDeclarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.2025101820263172026330124920263301248202633011192026330epublish41910902PMC1303595910.1186/s40510-026-00618-110.1186/s40510-026-00618-1Kapetanović A, Theodorou CI, Bergé SJ, Schols JGJH, Xi T. Efficacy of Miniscrew-Assisted Rapid Palatal Expansion (MARPE) in late adolescents and adults: a systematic review and meta-analysis. Eur J Orthod. 202;43:313–323.PMC818683733882127Southard TE, Marshall SD, Allareddy V, Shin K. Adult transverse diagnosis and treatment: A case-based review. Semin Orthod. 2019;25:69–108.10.1053/j.sodo.2019.02.008Andrucioli MCD, Matsumoto MAN. Transverse maxillary deficiency: treatment alternatives in face of early skeletal maturation. Dent Press J Orthod. 2020;25:70–9.10.1590/2177-6709.25.1.070-079.bboPMC707794532215481Ma S, Wang Y. Clinical outcomes of arch expansion with Invisalign: a systematic review. BMC Oral Health. 2023;23:1–17.10.1186/s12903-023-03302-6PMC1046444037620781Hartogsohn CR, Sonnesen L. Clear Aligner Treatment: Indications, Advantages, and Adverse Effects—A Systematic Review. Dent J (Basel). 2025;13:40.10.3390/dj13010040PMC1176416739851616Santucci V, Rossouw PE, Michelogiannakis D, El-Baily T, Feng C. Assessment of Posterior Dentoalveolar Expansion with Invisalign in Adult Patients. Int J Environ Res Public Health. 2023;20:4318.10.3390/ijerph20054318PMC1000196636901328Allahham DO, Kotsailidi EA, Barmak AB, Rossouw PE, El-Bialy T, Michelogiannakis D. Association between nonextraction clear aligner therapy and alveolar bone dehiscences and fenestrations in adults with mild-to-moderate crowding. Am J Orthod Dentofac Orthop. 2023;163:22–e324.10.1016/j.ajodo.2021.08.02236153200Santos CMDC, Pimenta CADM, Nobre MRC. The PICO strategy for the research question construction and evidence search. Rev Lat Am Enfermagem. 2007;15:508–11.10.1590/S0104-1169200700030002317653438Sterne JA, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016;355:i4919.10.1136/bmj.i4919PMC506205427733354Rocha AS, Gonçalves M, Oliveira AC, Azevedo RMS, Pinho T. Efficiency and Predictability of Coronal Maxillary Expansion Repercussion with the Aligners System: A Retrospective Study. Dent J (Basel). 2023;11:258.10.3390/dj11110258PMC1067057637999022Zhou N, Guo J. Efficiency of upper arch expansion with the Invisalign system. Angle Orthod. 2020;90:23–30.10.2319/022719-151.1PMC808706231368778Medeiros RB, Santos RF, Mendes-Miguel JA, Rothier EKC, Mendes FM, Dominguez GC. Accuracy of arch expansion with two thermoplastic materials in Invisalign® patients: EX30® and SmartTrack®. Dent Press J Orthod. 2024;29:e2423212.10.1590/2177-6709.29.2.e2423212.oarPMC1116395638865514Lione R, Paoloni V, Bartolommei L, Gazzani F, Meuli S, Pavoni C, et al. Maxillary arch development with Invisalign system: Analysis of expansion dental movements on digital dental casts. Angle Orthod. 2021;91:433–40.10.2319/080520-687.1PMC825975533570617Charalampakis O, Iliadi A, Ueno H, Oliver DR, Kim KB. Accuracy of clear aligners: A retrospective study of patients who needed refinement. Am J Orthod Dentofac Orthop. 2018;154:47–54.10.1016/j.ajodo.2017.11.02829957318Goh S, Dreyer C, Weir T. The predictability of the mandibular curve of Wilson, buccolingual crown inclination, and transverse expansion expression with Invisalign treatment. Am J Orthod Dentofac Orthop. 2023;163:109–16.10.1016/j.ajodo.2021.09.02036208968Galluccio G, De Stefano AA, Horodynski M, Impellizzeri A, Guarnieri R, Barbato E, et al. Efficacy and Accuracy of Maxillary Arch Expansion with Clear Aligner Treatment. Int J Environ Res Public Health. 2023;20:4634.10.3390/ijerph20054634PMC1000210036901642Morales-Burruezo I, Gandía-Franco JL, Cobo J, Vela-Hernández A, Bellot-Arcís C. Arch expansion with the Invisalign system: Efficacy and predictability. PLoS ONE. 2020;15:e0242979.10.1371/journal.pone.0242979PMC772826833301484de-la-Rosa-Gay C, Valmaseda-Castellón E, Figueiredo R, Camps-Font O. Factors affecting expansion predictability of clear aligner treatment. Clin Oral Investig. 2025;29:257.10.1007/s00784-025-06328-yPMC1201196140257582Riede U, Wai S, Neururer S, Reistenhofer B, Riede G, Besser K, et al. Maxillary expansion or contraction and occlusal contact adjustment: effectiveness of current aligner treatment. Clin Oral Investig. 2021;25:4671–9.10.1007/s00784-021-03780-4PMC831047333474622Houle JP, Piedade L, Todescan R, Pinheiro FHSL. The predictability of transverse changes with Invisalign. Angle Orthod. 2017;87:19–24.10.2319/122115-875.1PMC838859127304231Vidal-Bernárdez ML, Vilches-Arenas Á, Sonnemberg B, Solano-Reina E, Solano-Mendoza B. Efficacy and predictability of maxillary and mandibular expansion with the Invisalign® system. J Clin Exp Dent. 2021;13:e669–77.
10.4317/jced.58315PMC829116134306530O’Connor J, Weir T, Freer E, Kerr B. Clinical expression of programmed maxillary buccal expansion and buccolingual crown inclination with Invisalign EX30 and SmartTrack aligners and the effect of 1-week vs. 2-week aligner change regimes: A retrospective cohort study. Korean J Orthod. 2024;54:142–52.10.4041/kjod23.022PMC1112993638618737Keilig L, Brieskorn L, Schwarze J, Schupp W, Bourauel C, Konermann A. Treatment Efficiency of Maxillary and Mandibular Orovestibular Tooth Expansion and Compression Movements with the Invisalign® System in Adolescents and Adults. J Clin Med. 2024;13:1267.
10.3390/jcm13051267PMC1093225038592077Tien R, Patel V, Chen T, Lavrin I, Naoum S, Lee RJH, et al. The predictability of expansion with Invisalign: A retrospective cohort study. Am J Orthod Dentofac Orthop. 2023;163:47–53.10.1016/j.ajodo.2021.07.03236195544Solano-Mendoza B, Sonnemberg B, Solano-Reina E, Iglesias-Linares A. How effective is the Invisalign® system in expansion movement with Ex30′ aligners? Clin Oral Investig. 2017;21:1475–84.
10.1007/s00784-016-1908-y27435982Vidal Bernárdez ML, Vilches Arenas Á, Sonnemberg B, Solano Reina E, Solano Mendoza B. EX30 vs. SmartTrack materials in maxillary expansion with the Invisalign system. J Clin Orthod. 2022;56:343–50.35948311Bowman E, Bowman P, Weir T, Dreyer C, Meade MJ. Occlusal contacts and treatment with the Invisalign appliance: a retrospective analysis of predicted vs achieved outcomes. Angle Orthod. 2023;93:275–81.10.2319/102822-738.1PMC1011721436780260Basudan AA, Alsaggaf DH, Alsulaimani LA, Zawawi KH, El-Bialy TH, Hassan AH. Three-dimensional evaluation of the predictability of clear aligners in the treatment of maxillary transverse and anteroposterior tooth movements using cone beam computed tomography: A preliminary retrospective study. J Orthod Sci. 2025;14:42.10.4103/jos.jos_148_24PMC1255839141159156Haouili N, Kravitz ND, Vaid NR, Ferguson DJ, Makki L. Has Invisalign improved? A prospective follow-up study on the efficacy of tooth movement with Invisalign. Am J Orthod Dentofac Orthop. 2020;158:420–5.10.1016/j.ajodo.2019.12.01532620479Nogal-Coloma A, Yeste-Ojeda F, Rivero-Lesmes JC, Martin C. Predictability of Maxillary Dentoalveolar Expansion Using Clear Aligners in Different Types of Crossbites. Appl Sci (Basel). 2023;13:2963.10.3390/app13052963Suarez O, Alvarado-Lorenzo A, Calzadilla-Suárez E, Scuzzo G, León-Valencia J, Colino-Paniagua C, et al. Comparison of the Predictability of Dentoalveolar Expansion with Different Aligner Systems in Orthodontics: A Longitudinal Clinical Study in Adult Patients. Appl Sci (Basel). 2025;15:9074.10.3390/app15169074McGuinness LA, Higgins JPT. Risk-of-bias VISualization (robvis): An R package and Shiny web app for visualizing risk-of-bias assessments. Res Synth Methods. 2021;12:55–61.10.1002/jrsm.141132336025Caruso S, De Felice ME, Valenti C, Pagano S, Caruso S, Gatto R, et al. An evaluation of the Invisalign® Aligner Technique and consideration of the force system: a systematic review. Syst Rev. 2024;13:1–27.
10.1186/s13643-023-02437-5PMC1082123138281057Harris EF. Effects of Patient Age and Sex on Treatment: Correction of Class II Malocclusion with the Begg Technique. Angle Orthod. 2001;71:433–41.11771781Misawa-Kageyama Y, Kageyama T, Moriyama K, Kurihara S, Yagasaki H, Deguchi T, et al. Histomorphometric study on the effects of age on orthodontic tooth movement and alveolar bone turnover in rats. Eur J Oral Sci. 2007;115:124–30.10.1111/j.1600-0722.2007.00428.x17451502Lin C-L, Memorial G, Wang Y-C, Hsieh Y-J, Chen Y-F, Wang Y-C, et al. Clinical Effectiveness of Using Clear Aligners in Orthodontic Treatment. Taiwan J Orthod. 2020;32:2.10.38209/2708-2636.1001Condo’ R, Pazzini L, Cerroni L, Pasquantonio G, Lagana’ G, Pecora A, et al. Mechanical properties of two generations of teeth aligners: Change analysis during oral permanence. Dent Mater J. 2018;37:835–42.10.4012/dmj.2017-32329998941Fialho T, de Freitas KMS, Ohira ETB, de Souza JEP, de Oliveira RCG, de Oliveira RCG, et al. Comparison of the efficiency of initial dental alignment with Invisalign® aligners changed every 7 or 14 days in mature adults: Randomized clinical trial. Orthod Craniofac Res. 2024;27:544–51.
10.1111/ocr.1276638321815Al-Nadawi M, Kravitz ND, Hansa I, Makki L, Ferguson DJ, Vaid NR. Effect of clear aligner wear protocol on the efficacy of tooth movement: A randomized clinical trial. Angle Orthod. 2021;91:157–63.10.2319/071520-630.1PMC802848533296455Monisha J, Peter E. Efficacy of clear aligner wear protocols in orthodontic tooth movement—a systematic review. Eur J Orthod. 2024;46:cjae020.10.1093/ejo/cjae02038666743Karslı N, Ocak I, Akyıldız M, Gögen H, Dalci O. Evaluation of the effect of different attachment configurations on molar teeth in maxillary expansion with clear aligners – a finite element analysis. BMC Oral Health. 2024;24:921.10.1186/s12903-024-04544-8PMC1131636039123145Arveda N, Calza M, Castroflorio T, Garino F, Giancotti A, Varela JC, et al. Clear aligner orthodontic treatment: An international modified Delphi consensus study. Am J Orthod Dentofac Orthop. 2025;168:620–38.10.1016/j.ajodo.2025.07.01240970859D’Antò V, Oliva G, Nieri M, Clauser T, Statie MD, Nucci L, et al. Indications and limits of clear aligner therapy: an international modified Delphi consensus study. Prog Orthod. 2025;26:28.10.1186/s40510-025-00575-1PMC123217294076028241910547202603301536-37082026Mar30Annals of plastic surgeryAnn Plast SurgBenchmarking Large Language Models Against Web of Science: A Comparative Bibliometric Analysis on Panniculectomy.10.1097/SAP.0000000000004730As large language models (LLMs) grow in sophistication, their potential role in scientific writing is being explored with growing interest and caution. However, LLMs vary in their performance, contextual accuracy, and reliability. This study compares the outputs of 3 leading LLMs (ChatGPT-4o, Deepseek, and Claude 3.7) against a manually curated bibliometric analysis of the most highly cited panniculectomy articles.The 50 most highly cited panniculectomy publications were manually extracted from Web of Science (WoS) to serve as a reference data set. ChatGPT-4o, Deepseek, and Claude 3.7 Sonnet were each prompted to generate their own list of the 50 most cited panniculectomy articles. Outputs were compared across citation totals and averages, publication year trends, journal distribution, author co-occurrence, and article authenticity.The manual data set totaled 2494 citations (density: 49.8). ChatGPT-4o, Deepseek, and Claude 3.7 produced 2111 (42.2), 4736 (94.7), and 8592 (171.8) citations, respectively. Overlap with the manual list was limited: ChatGPT-4o (14.00%), Claude 3.7 (4.00%), Deepseek (0.00%) (P<0.001). "Plastic and Reconstructive Surgery" was the most cited journal across all outputs. Unique authors: manual (241), ChatGPT-4o (114), Deepseek (72), and Claude 3.7 (129). Article accuracy: ChatGPT-4o had 34.00% accurate, 26.00% confabulated, and 40.00% hallucinated articles. Claude 3.7: 4.00% accurate, 26.00% confabulated, and 70.00% hallucinated. Deepseek: 100.00% hallucinated (P<0.001). Year trends and journal representation varied notably from the manual set.Current LLMs struggle to replicate accurate bibliometric data. ChatGPT-4o performed best but still showed major limitations. WoS remains the gold standard, and LLM-generated outputs should be treated cautiously in bibliometric analyses.Copyright © 2026 Wolters Kluwer Health, Inc. All rights reserved.MangalRohanRUniversity of Miami Miller School of Medicine.JessupMarkM0009-0005-4682-9353University of Miami Miller School of Medicine.DesaiAnshumiADeWitt Department of Surgery, Division of Plastic Surgery, University of Miami Miller School of Medicine.KhandekarArchanADesai Sethi Urology Institute, University of Miami Miller School of Medicine, Miami, FL.PrasadSoumilSUniversity of Miami Miller School of Medicine.TadisinaKashyap KomarrajuKKDeWitt Department of Surgery, Division of Plastic Surgery, University of Miami Miller School of Medicine.SinghDevinderDDeWitt Department of Surgery, Division of Plastic Surgery, University of Miami Miller School of Medicine.engJournal Article20260330United StatesAnn Plast Surg78053360148-7043IMLLMartificial intelligencebibliometricpanniculectomyConflicts of interest and sources of funding: none declared.20251022026221202633012492026330124920263301042aheadofprint4191054710.1097/SAP.000000000000473000000637-990000000-01132Clapp B, Ponce J, Corbett J, et al. American Society for Metabolic and Bariatric Surgery 2022 estimate of metabolic and bariatric procedures performed in the United States. Surg Obes Relat Dis. 2024;20:425–431.Pestana IA, Campbell D, Fearmonti RM, et al. “Supersize” panniculectomy: indications, technique, and results. Ann Plast Surg. 2014;73:416–421.Payer M, Youngberg B, Pfister S. Panniculectomy—an option for people who are morbidly obese. AORN J. 2003;77:782–794; quiz 795, 797-798.Meyerowitz BR, Gruber RP, Laub DR. Massive abdominal panniculectomy. JAMA. 1973;225:408–409.Pratt JH, Irons GB. Panniculectomy and abdominoplasty. Am J Obstet Gynecol. 1978;132:165–168.Hernandez AB, Medina LG, Hueber PA, et al. Robotic simple prostatectomy plus panniculectomy and giant umbilical hernia repair. Int Braz J Urol. 2019;45:641.Aitzetmüller MM, Klietz ML, Dermietzel AF, et al. Robotic-assisted microsurgery and its future in plastic surgery. J Clin Med. 2022;11:3378.Mansoor M, Ibrahim AF. The transformative role of artificial intelligence in plastic and reconstructive surgery: challenges and opportunities. J Clin Med. 2025;14:2698.Telenti A, Auli M, Hie BL, et al. Large language models for science and medicine. Eur J Clin Invest. 2024;54:e14183.Joseph A, Joseph K, Joseph A. A pilot evaluation of the diagnostic accuracy of ChatGPT-3.5 for multiple sclerosis from case reports. Transl Neurosci. 2024;15:20220361.Athaluri SA, Manthena SV, Kesapragada VSRKM, et al. Exploring the boundaries of reality: investigating the phenomenon of artificial intelligence hallucination in scientific writing through ChatGPT references. Cureus. 2023;15:e37432.Alkaissi H, McFarlane SI. Artificial hallucinations in ChatGPT: implications in scientific writing. Cureus. 2023;15:e35179.Dumit J, Roepstorff A. AI hallucinations are a feature of LLM design, not a bug. Nature. 2025;639:38.Aljamaan F, Temsah MH, Altamimi I, et al. Reference hallucination score for medical artificial intelligence chatbots: development and usability study. JMIR Med Inform. 2024;12:e54345.Manley K, Salingaros S, Fuchsman AC, et al. Using ChatGPT to write a literature review on autologous fat grafting. J Plast Reconstr Aesthet Surg. 2025;105:292–304.Pranckutė R. Web of Science (WoS) and Scopus: the titans of bibliographic information in today’s academic world. Publications. 2021;9:12.Li K, Rollins J, Yan E. Web of Science use in published research and review papers 1997-2017: a selective, dynamic, cross-domain, content-based analysis. Scientometrics. 2018;115:1–20.Manoj Kumar L, George RJ, Anisha PS, et al. Bibliometric analysis for medical research. Indian J Psychol Med. 2023;45:277–282.Farquhar S, Kossen J, Kuhn L, et al. Detecting hallucinations in large language models using semantic entropy. Nature. 2024;630:625–630.Yilmaz BE, Gokkurt Yilmaz BN, Ozbey F. Artificial intelligence performance in answering multiple-choice oral pathology questions: a comparative analysis. BMC Oral Health. 2025;25:573.Liu X, Duan C, Kim MK, et al. Claude 3 Opus and ChatGPT with GPT-4 in dermoscopic image analysis for melanoma diagnosis: comparative performance analysis. JMIR Med Inform. 2024;12:e59273.Clusmann J, Kolbinger FR, Muti HS, et al. The future landscape of large language models in medicine. Commun Med (Lond). 2023;3:141.Keating M, Bollard SM, Potter S. Assessing the quality, readability, and acceptability of AI-generated information in plastic and aesthetic surgery. Cureus. 2024;16:e73874.Swisher AR, Wu AW, Liu GC, et al. Enhancing health literacy: evaluating the readability of patient handouts revised by ChatGPT’s large language model. Otolaryngol Head Neck Surg. 2024;171:1751–1757.Gomez-Cabello CA, Borna S, Pressman SM, et al. Large language models for intraoperative decision support in plastic surgery: a comparison between ChatGPT-4 and Gemini. Medicina (Kaunas). 2024;60:957.Brochu BM, Mirsky NA, Thaller SR. Evaluating ChatGPT’s efficacy in addressing common patient questions in plastic surgery consultations. Art Int Surg. 2024;4:411–426.Lim B, Seth I, Xie Y, et al. Exploring the unknown: evaluating ChatGPT’s performance in uncovering novel aspects of plastic surgery and identifying areas for future innovation. Aesthetic Plast Surg. 2024;48:2580–2589.Najafali D, Reiche E, Araya S, et al. Artificial intelligence augmentation: performance of GPT-4 and GPT-3.5 on the Plastic Surgery In-service Examination. Plast Reconstr Surg Glob Open. 2025;13:e6645.4190944120260330202603302168-81841822026FebCureusCureusBarriers to Healthcare Access and Utilization Among Immigrants in Host Countries: A Systematic Review of Qualitative and Quantitative Evidence.e104278e104278e10427810.7759/cureus.104278Immigrant populations frequently encounter barriers when accessing healthcare services, potentially affecting patient safety, healthcare utilization, and clinical outcomes. Understanding these barriers is essential for improving equitable and patient-centered care. A systematic review of qualitative and quantitative studies was conducted in accordance with PRISMA 2020 guidelines. PubMed/MedLINE, Embase, Cochrane Library, PsycINFO, EconLit, Web of Science (WoS), and CINAHL were searched from January 2005 to August 2023. Inductive thematic analysis was used to synthesize findings across studies. The review was not prospectively registered, included only English-language studies, and relied predominantly on qualitative evidence. Heterogeneity across study designs and healthcare settings may limit generalizability. The authors received no external funding for this study. Three interconnected themes consistently emerged: limited transcultural competence, language barriers, and discrimination in healthcare. Inadequate cultural competence was associated with communication difficulties and reduced care effectiveness. Language barriers contributed to miscommunication, delayed care, and increased healthcare utilization. Experiences of discrimination were linked to reduced trust in healthcare systems and poorer patient engagement. These factors negatively influenced patient safety, satisfaction, and clinical outcomes. Immigrant patients face persistent and interrelated barriers to healthcare access. Strengthening culturally responsive care, improving access to professional interpreter services, and addressing discriminatory practices are essential to improving patient safety, satisfaction, and clinical outcomes. Future research should evaluate targeted interventions aimed at improving communication, cultural competence, and healthcare equity.Copyright © 2026, Krupić et al.KrupićFeridFDepartment of Anaesthesiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, SWE.KrupićMelissaMDepartment of Gynaecology, Sahlgrenska University Hospital/Östra, Gothenburg, SWE.SahraSaidarabSDepartment of Maternity Care, Sahlgrenska University Hospital, Gothenburg, SWE.Institute of Health and Care Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, SWE.DerviševićEminaEDepartment of Forensic Medicine, University of Sarajevo, Faculty of Medicine, Sarajevo, BIH.SeffoNailNDepartment of Anaesthesiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, SWE.AlićJasminJUrology Clinic, Clinical Center University of Sarajevo, Sarajevo, BIH.engJournal ArticleReview20260226United StatesCureus1015967372168-8184access to healthcarecultural competencyemigrants and immigrantshealth services accessibilityhealthcare access disparitylanguage barrierConflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.2026225202633012512026330125020263306352026226epublish41909441PMC1303218410.7759/cureus.104278Eurostat. Health at a Glance: Europe 2018. [
Dec;
2025
]. 2018. https://health.ec.europa.eu/state-health-eu/health-glance-europe/health-glance-europe-2018_en https://health.ec.europa.eu/state-health-eu/health-glance-europe/health-glance-europe-2018_enOECD OECD, European Union. OECD, European Union. Health at a Glance: Europe 2018. Health at a Glance: Europe. [
Dec;
2025
]. 2018. https://www.oecd.org/en/publications/health-at-a-glance-europe-2018_health_glance_eur-2018-en.html https://www.oecd.org/en/publications/health-at-a-glance-europe-2018_health_glance_eur-2018-en.htmlHealth system response to war and displacement in Europe requires transformative actions and policies. [ Jan; 2025 ];Bozorgmehr K, McKee M, Rast E, Kazatchkine M, Zoidze A, Berdzuli N. Lancet Reg Health Eur. 2024 47:101122.PMC1167067839726727Patient experiences with general practice in Norway: A comparison of immigrant groups and the majority population following a national survey. Kjøllesdal M, Indseth T, Iversen HH, Bjertnaes O. BMC Health Serv Res. 2020;20:1106.PMC770810233256725Patient satisfaction: Concept analysis in the healthcare context. Ng JH, Luk BH. Patient Educ Couns. 2019;102:790–796.30477906Health status inequality among immigrants in Switzerland. Volken T, Rüesch P. Open J Prev Med. 2014;4:459–469.Inequalities in health care utilization among migrants and non-migrants in Germany: A systematic review. Klein J, von dem Knesebeck O. Int J Equity Health. 2018;17:160.PMC621160530382861What explains the inequalities in health care utilization between immigrants and non-migrants in Switzerland? Tzogiou C, Boes S, Brunner B. BMC Public Health. 2021;21:530.PMC797758633736623Global healthcare use by immigrants in Spain according to morbidity burden, area of origin, and length of stay. Gimeno-Feliu LA, Calderón-Larrañaga A, Diaz E, Poblador-Plou B, Macipe-Costa R, Prados-Torres A. BMC Public Health. 2016;16:450.PMC488282327230885Subjective perceptions of unmet need for health care in Europe among social groups: Findings from the European social survey (2014) special module on the social determinants of health. Fjær EL, Stornes P, Borisova LV, McNamara CL, Eikemo TA. Eur J Public Health. 2017;27:82–89.28355635Utilization of health care services by migrants in Europe-A systematic literature review. Graetz V, Rechel B, Groot W, Norredam M, Pavlova M. Br Med Bull. 2017;121:5–18.28108435The effects of reminders for colorectal cancer screening: Participation and inequality. Deding U, Henig AS, Torp-Pedersen C, Bøggild H. Int J Colorectal Dis. 2019;34:141–150.30386888Immigrant language proficiency, earnings, and language policies. Boyd M, Cao X. Can Stud Popul. 2009;36:63–86.The deep plunge: Luocha and experiences of earlier skilled immigrants from Mainland China in Toronto. Sakamoto I, Ku J, Wei Y. Qual Soc Work. 2009;8:427–447.Difficulties in using interpreters in clinical encounters as experienced by immigrants living in Sweden. Krupic F, Hellström M, Biscevic M, Sadic S, Fatahi N. J Clin Nurs. 2016;25:1721–1728.26879885Ethnic inequalities in access to and outcomes of healthcare: Analysis of the Health Survey for England. Nazroo JY, Falaschetti E, Pierce M, Primatesta P. J Epidemiol Community Health. 2009;63:1022–1027.19622520Fleurbaey M, Schokkaert E. Handbook of Health Economics. Vol. 2. Amsterdam: Elsevier; 2011. Equity in health and health care; pp. 1003–1092.The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Page MJ, McKenzie JE, Bossuyt PM, et al. BMJ. 2021;372:0.PMC800853933781348Segesten K. Dags för uppsats. Vol. 2. Lund: Studentlitteratur; 2022. Att välja ämne och modell för sitt examensarbete (Article in Swedish) pp. 65–78.Östlundh L. Dags för uppsats. Vol. 2. Lund: Studentlitteratur; 2022. Informationssökning (Article in Swedish) pp. 79–109.Friberg F. Dags för uppsats. Vol. 2. Lund: Studentlitteratur; 2022. Att göra en integrerande sammanställning av kvalitativ forskning (Article in Swedish) pp. 169–182.What do non-English-speaking patients value in acute care? Cultural competency from the patient's perspective: A qualitative study. Garrett PW, Dickson HG, Whelan AK, Roberto-Forero Roberto-Forero. Ethn Health. 2008;13:479–496.18850371Challenges of transcultural caring among health workers in Mashhad-Iran: A qualitative study. Amiri R, Heydari A, Dehghan-Nayeri N, Vedadhir AA, Kareshki H. Glob J Health Sci. 2015;8:203–211.PMC496566826925887Patients' evaluation of quality of care in general practice: What are the cultural and linguistic barriers? Harmsen JA, Bernsen RM, Bruijnzeels MA, Meeuwesen L. Patient Educ Couns. 2008;72:155–162.18485657Health care providers' perspectives of providing culturally competent care in the NICU. Hendson L, Reis MD, Nicholas DB. J Obstet Gynecol Neonatal Nurs. 2015;44:17–27.25581837Sociocultural and linguistic boundaries influencing intercultural communication between nurses and Moroccan patients in southern Spain: A focused ethnography. Plaza Del Pino FJ, Soriano E, Higginbottom GM. BMC Nurs. 2013;12:14.PMC366900723705680Nurse radiographers' experiences of communication with patients who do not speak the native language. Fatahi N, Mattsson B, Lundgren SM, Hellström M. J Adv Nurs. 2010;66:774–783.20423365Barriers and facilitators to cervical cancer screening among Pakistani and Somali immigrant women in Oslo: A qualitative study. Gele AA, Qureshi SA, Kour P, Kumar B, Diaz E. Int J Womens Health. 2017;9:487–496.PMC550554428740435Having cancer in a foreign country. Aelbrecht K, Pype P, Vos J, Deveugele M. Patient Educ Couns. 2016;99:1708–1716.27234174Managing precarity: Understanding Latinas' sexual and reproductive care-seeking in a Midwest emergent Latino community. Guerra-Reyes L, Palacios I, Ferstead A. Qual Health Res. 2021;31:871–886.33467985Immigrant parents' experience with the Swedish child health care system: A qualitative study. Mangrio E, Persson K. BMC Fam Pract. 2017;18:32.PMC533341028249606The experiences of Russian-speaking migrants in primary care consultations. Bachmann V, Völkner M, Bösnerr S, Donner-Banzhoff N. Dtsch Arztebl Int. 2014;111:871–876.PMC429823825597365"To be taken seriously": Women's reflections on how migration and resettlement experiences influence their healthcare needs during childbearing in Sweden. Robertson EK. Sex Reprod Healthc. 2015;6:59–65.25998872Sub-Saharan African immigrant women's experiences of (lack of) access to appropriate healthcare in the public health system in the Basque Country, Spain. Pérez-Urdiales I, Goicolea I, Sebastián MS, Irazusta A, Linander I. Int J Equity Health. 2019;18:59.PMC648065031014337Identifying the core components of cultural competence: Findings from a Delphi study. Jirwe M, Gerrish K, Keeney S, Emami A. J Clin Nurs. 2009;18:2622–2634.19538568Nurse-patient communication barriers in Iranian nursing. Anoosheh M, Zarkhah S, Faghihzadeh S, Vaismoradi M. Int Nurs Rev. 2009;56:243–249.19646175Difficulties in health care for female Turkish immigrants with type 2 diabetes: A qualitative study in Vienna. Biyikli Gültekin E. Wien Klin Wochenschr. 2017;129:337–344.28382526A qualitative study of language barriers between South African health care providers and cross-border migrants. Hunter-Adams J, Rother HA. BMC Health Serv Res. 2017;17:97.PMC528262928143514Experiences of advanced breast cancer among Latina immigrants: A qualitative pilot study. Glaser J, Coulter YZ, Thompson-Lastad A, Castro-Smyth L, Serrano E, Adler SR. J Immigr Minor Health. 2020;22:1287–1294.PMC788664132876850Learning to navigate the healthcare system in a new country: A qualitative study. Straiton ML, Myhre S. Scand J Prim Health Care. 2017;35:352–359.PMC573003329087232'If you feel that nobody wants you you'll withdraw into your own': Gypsies/travellers, networks and healthcare utilisation. Smith D, Ruston A. Sociol Health Illn. 2013;35:1196–1210.23574153Antenatal care for Somali-born women in Sweden: Perspectives from mothers, fathers and midwives. Ahrne M, Schytt E, Andersson E, Small R, Adan A, Essén B, Byrskog U. Midwifery. 2019;74:107–115.30953966A feminist exploration of Traveller women's experiences of maternity care in the Republic of Ireland. Reid B, Taylor J. Midwifery. 2007;23:248–259.17118498Exploring the health visiting service from the view of South Asian clients in England: A grounded theory study. Abdu L, Stenner K, Vydelingum V. Health Soc Care Community. 2016;24:596–604.25810116Kosovo-Serbs' experiences of seeking healthcare in a post-conflict and ethnically segregated health system. Luta X, Dræbel T. Int J Public Health. 2013;58:377–383.22945841Undocumented immigrants' and immigrant women's access to healthcare services in the Basque Country (Spain) Pérez-Urdiales I. Glob Health Action. 2021;14:1896659.PMC811841933975531Hospitalization experience of Muslim migrants in hospitals in southern Spain-Communication, relationship with nurses and culture. A focused ethnography. Plaza Del Pino FJ, Cala VC, Soriano Ayala E, Dalouh R. Int J Environ Res Public Health. 2020;17:2791.PMC721572432316654Cultural competence in the community health context: 'we don't have to reinvent the wheel'. Truong M, Gibbs L, Paradies Y, Priest N, Tadic M. Aust J Prim Health. 2017;23:342–347.28701258Patient safety culture and the cultural nursing care. Campelo CL, Sousa SMA, Silva LDC, Dias RS, Azevedo PR, Nunes FDO, Paiva SS. https://periodicos.ufpe.br/revistas/revistaenfermagem/article/view/235048 Rev Enferm UFPE Online. 2018;12:2500–2506.Contact with health-care service expectation and reality of situation experienced by immigrants seeking Swedish health-care. Krupic F, Sadic S, Fatahi N. https://pmc.ncbi.nlm.nih.gov/articles/PMC4851493/ Mater Sociomed. 2016;28:141–145.PMC485149327147923Factors beyond the language barrier in providing health care to immigrant patients. Fatahi N, Krupic F. https://pmc.ncbi.nlm.nih.gov/articles/PMC4779347/ Med Arch. 2016;70:61–65.PMC477934726980935Being immigrant in their own country: Experiences of Bosnians immigrants in contact with health care system in Bosnia and Herzegovina. Krupic F, Krupic R, Jasarevic M, Sadic S, Fatahi N. https://pmc.ncbi.nlm.nih.gov/articles/PMC4412908/ Mater Sociomed. 2015;27:4–9.PMC441290825945077Racism in European health care: Structural violence and beyond. Hamed S, Thapar-Björkert S, Bradby H, Ahlberg BM. Qual Health Res. 2020;30:1662–1673.PMC741027532546076Increased perinatal mortality among sub-Saharan immigrants in a city-population in Sweden. Essén B, Hanson BS, Ostergren PO, Lindquist PG, Gudmundsson S. https://pubmed.ncbi.nlm.nih.gov/10993096/ Acta Obstet Gynecol Scand. 2000;79:737–743.10993096Hedelin B, Jormfeldt H, Svedberg P. Omvårdnadens grunder, 2nd ed. Vol. 36. Lund: Studentlitteratur; 2014. Hälsobegreppet - synen på hälsa och sjuklighet (Article in Swedish) p. 185.Swedish Parliament: Discrimination Act (SFS2008567) Swedish Parliament: Discrimination Act (SFS. [
Dec;
2025
]. https://www.do.se/choose-language/english/discrimination-act-2008567 https://www.do.se/choose-language/english/discrimination-act-200856741906345202603301724-60162026Mar29European journal of ophthalmologyEur J OphthalmolGlobal trends and developments in research on visually impaired athletes from 1960 to 2025: A bibliometric analysis.112067212614195801120672126141958010.1177/11206721261419580IntroductionThis study investigates global research trends on visually impaired athletes (VIA) from 1960 to 2025, focusing on the evolution, productivity, and influence of scholarly output. By examining contributions from countries, institutions, journals, and authors, the study highlights emerging themes and collaborative networks that shape this specialized field.MethodsA bibliometric analysis was conducted using publications retrieved from the Web of Science (WoS) Core Collection. Quantitative indicators included publication count, citation frequency, average citations per article, and the Hirsch index. VOSviewer (version 1.6.19) was used to map keyword co-occurrence, co-citation, and co-authorship collaboration networks, providing insight into thematic development and international collaboration.ResultsThe USA led research output with 112 publications (28.3%), followed by Brazil (53, 13.4%) and the UK (48, 12.2%). Vrije Universiteit Amsterdam (19 publications) and Amsterdam Movement Sciences (15) were the most productive institutions. The Journal of Visual Impairment and Blindness was the most prolific journal, while Mann, DL, was the most productive author, with 19 publications and 381 citations. Research activity increased markedly over time, averaging 11 publications annually between 2010 and 2015 and peaking at 47 publications in 2021.ConclusionResearch on VIA has increased significantly, especially in the past two decades, reflecting growing recognition of the role of sport in rehabilitation, social inclusion, and psychological well-being. The identification of influential authors, institutions, and collaboration patterns provides a foundation for advancing evidence-based practices and shaping future policy and research directions in support of VIA.ChenKai-YangKY0000-0002-0540-1079Department of General Medicine, Chang Gung Memorial Hospital (Linkou branch), Taoyuan, Taiwan.38014School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.ChanHoi-ChunHCSchool of Pharmacy, China Medical University, Taichung, Taiwan.ChanChi-MingCMDepartment of Ophthalmology, Cardinal Tien Hospital, New Taipei City, Taiwan.38012School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan.engJournal Article20260329United StatesEur J Ophthalmol91107721120-6721IMVisually impaired athletebibliometric analysiskeyword co-occurrencepara-sportsvisual disability202633062920263306292026330113aheadofprint4190634510.1177/1120672126141958041904366202603281573-742X2026Mar28Journal of thrombosis and thrombolysisJ Thromb ThrombolysisAngio-Seal plug-based versus dual ProGlide for transfemoral hemostasis in transcatheter aortic valve replacement: a systematic review and meta-analysis.10.1007/s11239-026-03261-zTranscatheter aortic valve replacement (TAVR) carries risks of vascular and bleeding complications. We aimed to study the effectiveness of Angio-Seal combined with Perclose ProGlide versus dual Perclose ProGlide following transfemoral TAVR. We searched PubMed, Cochrane Library, Scopus, and WOS from inception until November 2024 for studies comparing Parclose ProGlide with Angio-Seal versus dual Parclose ProGlide in transfemoral TAVR. The primary outcome was major vascular complications, while other secondary outcomes were bleeding complications, the need for additional vascular closure device (VCD), minor vascular complications, unplanned surgical intervention, mortality, hematoma, pseudoaneurysm, and dissection. Dichotomous outcomes were pooled and analyzed using odds ratio (OR) with 95% confidence interval (CI) via the DerSimonian-Laird random-effect model. Seven studies (two RCTs and five observational studies) comprising 1,766 patients were included. In RCT-only analyses, single Perclose ProGlide combined with Angio-Seal showed no significant difference compared with dual Perclose ProGlide in major vascular complications (OR = 0.54, 95% CI [0.28-1.04], P = 0.07) or major/life-threatening bleeding (OR = 0.66, 95% CI [0.25-1.74], P = 0.40). However, it significantly reduced the need for additional vascular closure devices (OR = 0.11, 95% CI [0.05-0.24], P < 0.01) and minor vascular complications (OR = 0.52, 95% CI [0.38-0.72], P < 0.01). For dual Perclose ProGlide combined with Angio-Seal, no RCTs were available; observational evidence suggested a reduction in major/life-threatening bleeding compared to dual Perclose ProGlide (OR = 0.43, 95% CI [0.21 to0.92], p = 0.03), while there was no significant difference between the two groups upon excluding Costa et al. (EuroIntervention, 17:728-735, 2021) (OR = 0.56, 95% CI [0.26 to 1.21], p = 0.14). Based on the available RCTs, the combination of single Perclose ProGlide with Angio-Seal during transfemoral TAVR significantly reduces the need for additional vascular closure devices. It may also reduce minor vascular complications, but this effect is not consistently robust. The pooled analysis of observational studies suggests a potential benefit of dual Perclose ProGlide combined with Angio-Seal for major/life-threatening bleeding. However, this finding was not robust in sensitivity analyses and no RCTs have evaluated this strategy.© 2026. The Author(s).AlnomaniYousef RadwanYRFaculty of Medicine, Benha University, Benha, Egypt. yousefradwan2429@gmail.com.AlgazarMansourMFaculty of Medicine, Al-Azhar University, Asyut, Egypt.Medical Research group of Egypt (MRGE), Cairo, Egypt.OmarMohamed MosadMMFaculty of Medicine, Cairo University, Cairo, Egypt.Medical Research group of Egypt (MRGE), Cairo, Egypt.AldemerdashMohamed AMAFaculty of Medicine, Sohag university, Sohag, Egypt.Medical Research group of Egypt (MRGE), Cairo, Egypt.HenesKerollosKDepartment of Cardiology, El-Mabarra Health Insurance Hospital, Asyut, Egypt.Medical Research group of Egypt (MRGE), Cairo, Egypt.ArafaAmrADepartment of Nephrology, Alexandria University, Alexandria, Egypt.RagehBushra MuneerBMDepartment of Physiology, Sana'a University, Sana'a, Yemen.Medical Research group of Egypt (MRGE), Cairo, Egypt.AbdelazizAhmedADivision of Cardiology, Montefiore Health System/Albert Einstein College of Medicine, Bronx, NY, USA.Medical Research group of Egypt (MRGE), Cairo, Egypt.engJournal ArticleReview20260328NetherlandsJ Thromb Thrombolysis95020180929-5305IMAngio-SealClosure devicePerclose ProGlidePlug-basedSuture-basedTAVRDeclarations. Competing interests: The authors declare no competing interests. Consent for publication: Not applicable. Ethical approval: Not applicable.2025122320262272026329735202632973520263282319aheadofprint4190436610.1007/s11239-026-03261-z10.1007/s11239-026-03261-zArora S, Misenheimer JA, Ramaraj R (2017) Transcatheter aortic valve replacement: comprehensive review and present status. Tex Heart Inst J 44:29–3810.14503/THIJ-16-5852282652105317356Moncla L-HM, Briend M, Bossé Y, Mathieu P (2023) Calcific aortic valve disease: mechanisms, prevention and treatment. Nat Rev Cardiol 20:546–55910.1038/s41569-023-00845-736829083Joseph J, Naqvi SY, Giri J, Goldberg S (2017) Aortic stenosis: pathophysiology, diagnosis, and therapy. Am J Med 130:253–26310.1016/j.amjmed.2016.10.00527810479Avvedimento M, Angellotti D, Ilardi F, Leone A, Scalamogna M, Castiello DS et al (2023) Acute advanced aortic stenosis. Heart Fail Rev 28(5):1101–111110.1007/s10741-023-10312-73708396610403405Quagliana A, Montarello NJ, Willemen Y, Bække PS, Jørgensen TH, De Backer O et al (2023) Commissural alignment and coronary access after transcatheter aortic valve replacement. J Clin Med 12:213610.3390/jcm120621363698313910056242Rk STJL, M B, Rh FMB (2013) H, Management of vascular access in transcatheter aortic valve replacement: part 1: basic anatomy, imaging, sheaths, wires, and access routes. JACC Cardiovasc Interv 6(7):643–653Agarwal S, Tuzcu EM, Stewart W, Bajaj NS, Svensson LG, Kapadia SR (2013) Comparison of multicenter registries and randomized control trials for transcatheter aortic valve replacement (TAVR). Indian Heart J 65:40010.1016/j.ihj.2013.06.007239930013860977Qt B, Dm K, Hc AB (2010) H. Double wire angio-seal closure technique after balloon aortic valvuloplasty. Catheter Cardiovasc Interv 75(4):488–492Sf L, Jm S, Hl K, G N, Ms L (2019) Acute femoral occlusion after adjunctive angio-seal usage in vascular closure following transcatheter aortic valve replacement. JACC Case Rep 1(4):549–552Cf Y, Hl K, Ty K, Ck C, Ch T, Cc H et al (2024) Dual ProGlide vs ProGlide and Angio-Seal for femoral access hemostasis after transcatheter aortic valve replacement: a randomised comparative trial. Can J Cardiol. https://doi.org/10.1016/j.cjca.2024.09.00110.1016/j.cjca.2024.09.001Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372:n7110.1136/bmj.n71337820578005924Kappetein AP, Head SJ, Généreux P, Piazza N, van Mieghem NM, Blackstone EH et al (2012) Updated standardized endpoint definitions for transcatheter aortic valve implantation: the Valve Academic Research Consortium-2 consensus document (VARC-2). Eur J Cardiothorac Surg 42:S45–6010.1093/ejcts/ezs53323026738VARC-3 WRITING COMMITTEE, Généreux P, Piazza N, Alu MC, Nazif T, Hahn RT et al (2021) Valve Academic Research Consortium 3: updated endpoint definitions for aortic valve clinical research. Eur Heart J 42:1825–185710.1093/eurheartj/ehaa799Rheude T, Ruge H, Altaner N, Pellegrini C, Alvarez Covarrubias H, Mayr NP et al (2024) Comparison of strategies for vascular ACCESS closure after transcatheter aortic valve implantation: the ACCESS-TAVI randomized trial. Eur Heart J 46(7):635–645Sarathy K, Patel KP, Jones DM, Akhtar M, Khanna V, Broyd C et al (2021) Large bore vascular access closure device strategies. Struct Heart 5:186–19110.1080/24748706.2021.1872818Ko T-Y, Kao H-L, Liu Y-J, Yeh C-F, Huang C-C, Chen Y-H et al (2019) Intentional combination of ProGlide and Angio-Seal for femoral access haemostasis in transcatheter aortic valve replacement. Int J Cardiol 293:76–7910.1016/j.ijcard.2019.05.05531155328Cakal B, Cakal S, Karaca O, Yilmaz FK, Gunes HM, Yildirim A et al (2022) Angio-Seal used as a bailout for incomplete hemostasis after dual perclose ProGlide deployment in transcatheter aortic valve implantation. Tex Heart Inst J 49:e217684365159329809091Costa G, Valvo R, Picci A, Criscione E, Reddavid C, Motta S et al (2021) An upfront combined strategy for endovascular haemostasis in transfemoral transcatheter aortic valve implantation. EuroIntervention 17:728–73510.4244/EIJ-D-20-01125335894119724957Kiramijyan S, Magalhaes MA, Ben-Dor I, Koifman E, Escarcega RO, Baker NC et al (2016) The adjunctive use of Angio-Seal in femoral vascular closure following percutaneous transcatheter aortic valve replacement. EuroIntervention 12:88–9310.4244/EIJV12I1A1627173868Mach M, Okutucu S, Kerbel T, Arjomand A, Fatihoglu SG, Werner P et al (2021) Vascular complications in TAVR: incidence, clinical impact, and management. J Clin Med 10:504610.3390/jcm10215046347685658584339Sherwood MW, Xiang K, Matsouaka R, Li Z, Vemulapalli S, Vora AN et al (2020) Incidence, temporal trends, and associated outcomes of vascular and bleeding complications in patients undergoing transfemoral transcatheter aortic valve replacement: insights from the Society of Thoracic Surgeons/American College of Cardiology Transcatheter Valve Therapies Registry. Circ Cardiovasc Interv 13:e00822710.1161/CIRCINTERVENTIONS.119.00822731937138Health C for, D (2023) and R. Perclose ProStyle Suture-Mediated Closure and Repair System and Perclose ProGlide Suture-Mediated Closure Systems – P960043/S118. FDAPerclose ProStyle Suture-Mediated Closure System Overview https://www.cardiovascular.abbott/int/en/hcp/products/peripheral-intervention/vessel-closure/perclose-prostyle-suture-mediated-closure-system/overview.html . Accessed 1 Dec 2024Berti S, Bedogni F, Giordano A, Petronio AS, Iadanza A, Bartorelli AL et al (2020) Efficacy and Safety of ProGlide Versus Prostar XL Vascular Closure Devices in Transcatheter Aortic Valve Replacement: The RISPEVA Registry. J Am Heart Assoc 9:e01804210.1161/JAHA.120.018042331035457763424Xiang Y, Chen C, Zhao J, Ma Y, Huang B, Wu Z (2023) Vascular complications of ProGlide versus Prostar in transcatheter aortic valve replacement (TAVR) procedures: meta-analysis. BJS Open 7:zrad06110.1093/bjsopen/zrad0613749896610373905Hollowed J, Akhondi A, Rabbani A, Oncel D, Suh W, Rafique A et al (2022) Single versus double Perclose techniques for vascular closure during transfemoral transcatheter aortic valve replacement. Catheter Cardiovasc Interv 99:2125–213035420254Gmeiner JMD, Linnemann M, Steffen J, Scherer C, Orban M, Theiss H et al (2022) Dual ProGlide versus ProGlide and FemoSeal for vascular access haemostasis after transcatheter aortic valve implantation. EuroIntervention 18:812–81910.4244/EIJ-D-22-00311359038469724847Samy M, Landt M, Mankerious N, Kurniadi A, Alotaibi S, Toelg R et al (2024) ProGlide-AngioSeal versus ProGlide-FemoSeal for vascular access hemostasis posttranscatheter aortic valve implantation. Catheter Cardiovasc Interv 104:1251–12593937838441902978202603281573-25842026Mar28International urology and nephrologyInt Urol NephrolBibliometric analysis of Alport syndrome: genetic foundations, clinical implications, and the transition to precision medicine.10.1007/s11255-026-05116-1Alport syndrome is a rare genetic disorder characterized by progressive renal impairment, hearing loss, and ocular abnormalities. Our study aimed to elucidate global research trends on Alport syndrome through bibliometric analysis.A comprehensive bibliometric analysis of Alport syndrome literature (1990-2025) was performed using WoS and Scopus. After merging and de-duplication (n = 2371), CiteSpace, VOSviewer, and Bibliometrix facilitated network visualization, co-occurrence, co-citation, and historiographic evaluations.Research on Alport syndrome has grown exponentially since 1990 (2371 documents; 6.04% annual growth), dominated by original articles and moderate international collaboration. The USA leads in output, collaboration, and citations; China's volume is rising with evolving global impact. A hub-and-cluster author structure (e.g., Savige, Gross, Kashtan) publishes mainly in nephrology journals (Kidney International, JASN, Pediatric Nephrology), with interdisciplinary citation flows. Co-citation and bursts trace a shift from GBM/type IV collagen genetics to precision diagnostics and renoprotective therapy. Landmark studies established COL4 mutations, genotype-phenotype links, and early ACE inhibition, defining a gene-structure-phenotype-therapy axis.Alport syndrome research has progressed from genetic discovery toward more integrative and precision-focused approaches, supported by expanding global collaboration and contributions from key nephrology journals. Emerging priorities-including earlier diagnosis, renoprotective strategies, and advanced molecular techniques-reflect a sustained shift toward more personalized research directions that may inform future clinical management.© 2026. The Author(s), under exclusive licence to Springer Nature B.V.TianHuiHBeijing Hepingli Hospital, Beijing, China.ZhangMinhengMDepartment of Gerontology, The First People's Hospital of Jinzhong, No. 689 Huitong South Road, Jinzhong City, 030600, Shanxi Province, China. zmhfhx@163.com.engJournal Article20260328NetherlandsInt Urol Nephrol02625210301-1623IMAlport syndromeBibliometric analysisGenotype–phenotype correlationNext-generation sequencingRAAS blockadeDeclarations. Conflict of interest: The authors declare no competing interests. Ethical approval: An ethics statement was not required for this study type as it is based exclusively on published literature. Clinical trial number: Not applicable. This article does not contain any studies with human participants performed by any of the authors.202621020263212026329734202632973420263281216aheadofprint4190297810.1007/s11255-026-05116-110.1007/s11255-026-05116-1Omachi K et al (2025) Anticodon-edited tRNA enables translational readthrough of COL4A5 premature termination codons. PLoS ONE 20(12):e033080410.1371/journal.pone.03308044141782112716684Çakmak-Cengiz E, Karaman-Atasever F, Can E (2025) Case report: surgical and clinical results in bilateral lenticonus due to Alport syndrome. Rom J Ophthalmol 69(3):414–4174118977212580665Matsuzaki S et al (2025) Clinical features of hearing loss and genotype-phenotype correlations in Alport syndrome caused by COL4A4 or COL4A5 variants. Sci Rep 15(1):4380310.1038/s41598-025-27772-64139842112705733Arman F et al (2025) Alport syndrome: a case of delayed diagnosis through genetic testing. Cureus 17(11):e959274134132712670061Sarfraz Z et al (2025) Systematic review of management strategies for Alport syndrome: implications for male patients. Health Sci Rep 8(4):e7059510.1002/hsr2.705954016592711955716Omachi K, Miner JH (2019) Alport syndrome therapeutics: ready for prime-time players. Trends Pharmacol Sci 40(11):803–80610.1016/j.tips.2019.07.01231455555Okazaki A, Ott J (2022) Machine learning approaches to explore digenic inheritance. Trends Genet 38(10):1013–101810.1016/j.tig.2022.04.00935581032Chang AR et al (2025) Using large genomic biobanks to generate insights into genetic kidney disease. Semin Nephrol 45(5):15165110.1016/j.semnephrol.2025.1516514065191812411989Dunkley JC et al (2021) Carvedilol and exercise combination therapy improves systolic but not diastolic function and reduces plasma osteopontin in Col4a3(-/-) Alport mice. Am J Physiol Heart Circ Physiol 320(5):H1862-h187210.1152/ajpheart.00535.2020337699158163658Chen C (2006) CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inf Sci Technol 57(3):359–37710.1002/asi.20317Chen C (2017) Science mapping: a systematic review of the literature. J Data Inf Sci 2(2):1–40van Eck NJ, Waltman L (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84(2):523–53810.1007/s11192-009-0146-320585380Egghe L (2006) Theory and practise of the g-index. Scientometrics 69(1):131–15210.1007/s11192-006-0144-7Hirsch JE (2005) An index to quantify an individual’s scientific research output. Proc Natl Acad Sci USA 102(46):16569–1657210.1073/pnas.0507655102162759151283832Aria M, Cuccurullo C (2017) bibliometrix: an R-tool for comprehensive science mapping analysis. J Informetr 11(4):959–97510.1016/j.joi.2017.08.007Barker DF et al (1990) Identification of mutations in the COL4A5 collagen gene in Alport syndrome. Science 248(4960):1224–122710.1126/science.23494822349482Mochizuki T et al (1994) Identification of mutations in the α3(IV) and α4(IV) collagen genes in autosomal recessive Alport syndrome. Nat Genet 8(1):77–8210.1038/ng0994-777987396Jais JP et al (2003) X-linked Alport syndrome: natural history and genotype-phenotype correlations in girls and women belonging to 195 families: a “European Community Alport Syndrome Concerted Action” study. J Am Soc Nephrol. https://doi.org/10.1097/01.ASN.0000090034.71205.7410.1097/01.ASN.0000090034.71205.7414514738Bekheirnia MR et al (2010) Genotype–phenotype correlation in X-linked alport syndrome. J Am Soc Nephrol. https://doi.org/10.1681/ASN.200907078410.1681/ASN.2009070784203788212865738Gross O et al (2012) Early angiotensin-converting enzyme inhibition in Alport syndrome delays renal failure and improves life expectancy. Kidney Int 81(5):494–50110.1038/ki.2011.40722166847Savige J et al (2013) Expert guidelines for the management of Alport syndrome and thin basement membrane nephropathy. J Am Soc Nephrol. https://doi.org/10.1681/ASN.201202014810.1681/ASN.2012020148240526343839543Kashtan CE et al (2018) Alport syndrome: a unified classification of genetic disorders of collagen IV α345: a position paper of the Alport Syndrome Classification Working Group. Kidney Int 93(5):1045–105110.1016/j.kint.2017.12.01829551517Gibson J et al (2021) Prevalence estimates of predicted pathogenic COL4A3–COL4A5 variants in a population sequencing database and their implications for Alport syndrome. J Am Soc Nephrol. https://doi.org/10.1681/ASN.202007106510.1681/ASN.2020071065344005398729840Nozu K et al (2019) A review of clinical characteristics and genetic backgrounds in Alport syndrome. Clin Exp Nephrol 23(2):158–16810.1007/s10157-018-1629-430128941Kashtan CE (2021) Alport syndrome: achieving early diagnosis and treatment. Am J Kidney Dis 77(2):272–27910.1053/j.ajkd.2020.03.02632712016Savige J et al (2022) Guidelines for genetic testing and management of Alport syndrome. Clin J Am Soc Nephrol 17(1):143–15410.2215/CJN.0423032134930753Servais A et al (2021) Cystinuria: clinical practice recommendation. Kidney Int 99(1):48–5810.1016/j.kint.2020.06.03532918941Xie XH et al (2025) LRRC56 deficiency cause motile ciliopathies in humans and mice. Front Genet 16:165806310.3389/fgene.2025.16580634147764312753093Abramenko IV et al (2025) The IGLV3-21 light chain analysis in chronic lymphocytic leukemia patients. Probl Radiac Med Radiobiol 30:299–30910.33145/2304-8336-2025-30-299-309Du H et al (2025) An integrated platform for concurrent structural and single-nucleotide variants improves copy-number detection and reveals pathogenic alleles in undiagnosed Mendelian families. Genome Med. https://doi.org/10.1186/s13073-025-01593-810.1186/s13073-025-01593-84147002612866024Liu N et al (2023) Case report: preimplantation genetic testing for X-linked Alport syndrome caused by variation in the COL4A5 gene. Front Pediatr 11:117701910.3389/fped.2023.11770193763580010448762Zhao Y, Zheng Q, Xie J (2024) Exploration of gene therapy for Alport syndrome. Biomedicines 12:1159. https://doi.org/10.3390/biomedicines1206115910.3390/biomedicines120611593892736611200676Andersen K et al (2017) Intestinal dysbiosis, barrier dysfunction, and bacterial translocation account for CKD–related systemic inflammation. J Am Soc Nephrol. https://doi.org/10.1681/ASN.201511128510.1681/ASN.201511128527151924Liu JE (2025) Advancing cardio-oncology: precision-guided cardiovascular care for oncology patients. Cardiol Plus 10(4):241–24510.1097/CP9.00000000000001424147751912753144Lee SY, Jeffery AD (2026) From the iron lung to artificial intelligence: integrating new technology into critical care. Am J Crit Care 35(1):70–7610.4037/ajcc2026154414757804189727620260328202603302227-90321462026Mar23Healthcare (Basel, Switzerland)Healthcare (Basel)Digital Stress: Insights from Bibliometric, Scientometric, Meta-Analytic and Thematic Analyses.82310.3390/healthcare14060823Digital stress, the psychological strain from constant connectivity, is a growing challenge, but the research field remains conceptually fragmented. This study aims to (1) map the evolution of digital stress research via bibliometric and scientometric analyses; (2) quantify measurement consistency through a meta-analysis of the Digital Stress Scale (DSS); and (3) synthesize thematic trends to clarify the construct's boundaries. A multi-method review was conducted, integrating bibliometric analysis of 215 documents (Scopus/WoS), Google Ngram analysis, a random-effects meta-analysis of 10 DSS studies (n = 8572), and a thematic analysis of keyword co-occurrence. Bibliometrics and Ngram analysis show the field is maturing, with publications rising sharply post-2020, distinguishing it from 'technostress.' The construct evolved from biomedical/engineering uses to a psychosocial concept linked to 'social media' and 'mental health.' The meta-analysis found a moderate pooled mean stress level (2.45 on a 1-5 scale, 95% CI: 2.12-2.78), falling within the 'average' range of U.S. norms. High heterogeneity (I2 = 99.7%) confirmed that cultural and contextual factors significantly moderate stress levels. Thematic analysis identified four key dimensions: conceptual ambiguity, contextual moderators, the digital transformation paradox, and digital well-being. Digital stress is a distinct, multidimensional construct encompassing social-evaluative pressures beyond original technostress models. This review consolidates its theoretical boundaries and confirms the DSS's psychometric consistency, highlighting digital stress as a critical, context-dependent factor in human adaptation to technology.AlmakrobAhmed YahyaAYDepartment of English Language and Literature, College of Sciences and Humanities, Prince Sattam bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia.AlduaisAhmedA0000-0003-0837-4915Department of Psychology, Norwegian University of Science and Technology, 7034 Trondheim, Norway.engPSAU/2025/03/34254Prince Sattam Bin Abdulaziz UniversityJournal ArticleReview20260323SwitzerlandHealthcare (Basel)1016665252227-9032Digital Stress Scalebibliometric analysisdigital stressdigital well-beingmeta-analysisscientometricstechnostressThe authors declare no conflicts of interest.2026217202631220263182026328641202632864020263281182026323epublish41897276PMC1302723810.3390/healthcare14060823healthcare14060823Hall J.A., Steele R.G., Christofferson J.L., Mihailova T. Development and initial evaluation of a multidimensional digital stress scale. Psychol. Assess. 2021;33:230–242. doi: 10.1037/pas0000979.10.1037/pas000097933507798Gimpel H., Lanzl J., Regal C., Urbach N. Coping with digital stress in the workplace. Inf. Syst. Res. 2022;36:896–915. doi: 10.1287/isre.2022.0691.10.1287/isre.2022.0691Cioffi G., Balducci C., Toderi S. Technostress and organizational outcomes during digital transformation. Int. J. Environ. Res. Public Health. 2023;22:267. doi: 10.3390/ijerph22020267.10.3390/ijerph22020267Würtenberger A., Groneberg D.A., Mache S. Technostress and health in healthcare workers. BMC Health Serv. Res. 2025;25:535. doi: 10.1186/s12913-025-12653-5.10.1186/s12913-025-12653-5PMC1198719140217285Feng X., Guan W., Xu E. Digital stress and professional compromise among inclusive preschool teachers in China: A chain mediation model based on negative automatic thoughts and career adaptability. Acta Psychol. 2025;258:105261. doi: 10.1016/j.actpsy.2025.105261.10.1016/j.actpsy.2025.10526140669361Yang D., Liu J., Wang H., Chen P. Technostress among teachers: A systematic literature review and future research agenda. Comput. Hum. Behav. 2025;168:108619. doi: 10.1016/j.chb.2025.108619.10.1016/j.chb.2025.108619Ayyagari R., Grover V., Purvis R. Technostress: Technological antecedents and implications. MIS Q. 2011;35:831–858. doi: 10.2307/41409963.10.2307/41409963Weinstein E.C., Selman R.L. Digital stress: Adolescents’ personal accounts. New Media Soc. 2016;18:391–409. doi: 10.1177/1461444814543989.10.1177/1461444814543989Hefner D., Vorderer P. The Routledge Handbook of Media Use and Well-Being. Taylor & Francis; Abingdon, UK: 2016. Digital stress: Permanent connectedness and multitasking; pp. 237–249.10.4324/9781315714752Aria M., Cuccurullo C. Bibliometrix: An R-tool for comprehensive science mapping analysis. J. Informetr. 2017;11:959–975. doi: 10.1016/j.joi.2017.08.007.10.1016/j.joi.2017.08.007Donthu N., Kumar S., Mukherjee D., Pandey N., Lim W.M. How to conduct a bibliometric analysis: An overview and guidelines. J. Bus. Res. 2021;133:285–296. doi: 10.1016/j.jbusres.2021.04.070.10.1016/j.jbusres.2021.04.070Salazar-Concha C., Ficapal-Cusí P., Boada-Grau J., Camacho L. Analyzing the evolution of technostress: A science mapping approach. Heliyon. 2021;7:e06726. doi: 10.1016/j.heliyon.2021.e06726.10.1016/j.heliyon.2021.e06726PMC806528833912710Urukovičová N., Rošková E., Schraggeová M., Smoroň J. Psychometric properties of the Technostress Creators Inventory among employed Slovak respondents. Comput. Hum. Behav. Rep. 2023;12:100324. doi: 10.1016/j.chbr.2023.100324.10.1016/j.chbr.2023.100324Cadieux N., Camille A., Cadieux J., Gouin M., Morin É., Fournier P. Technostress or reaction to techno-stressors? Validation of bilingual techno-stressors index (TSI-II) and a second-order formative model of techno-distress among Canadian legal professionals. Comput. Hum. Behav. Rep. 2024;16:100485. doi: 10.1016/j.chbr.2024.100485.10.1016/j.chbr.2024.100485Yang Y., Shamim S., De Massis A., Gao D. Defensive routines as coping mechanisms against technostress. Technol. Forecast. Soc. Change. 2025;216:124143. doi: 10.1016/j.techfore.2025.124143.10.1016/j.techfore.2025.124143Nick E., Kilic Z., Nesi J., Telzer E., Lindquist K., Prinstein M. Adolescent digital stress: Frequencies, correlates, and longitudinal association with depressive symptoms. J. Adolesc. Health. 2022;70:336–339.. doi: 10.1016/j.jadohealth.2021.08.025.10.1016/j.jadohealth.2021.08.025PMC1015343134666954De Groote D., Van Ouytsel J. Digital stress within early adolescents’ friendships: A focus group study from Belgium. Telemat. Inform. 2022;73:101877. doi: 10.1016/j.tele.2022.101877.10.1016/j.tele.2022.101877Qiu Y., Zhao X., Liu J., Li Z., Wu M., Qiu L., Xiong Z., Wang X., Yang F. Understanding the relationship between smartphone distraction, social withdrawal, digital stress, and depression among college students: A cross-sectional study in Wuhan, China. Heliyon. 2024;10:e35465. doi: 10.1016/j.heliyon.2024.e35465.10.1016/j.heliyon.2024.e35465PMC1133487639166051Yang C., Smith C. Digital social multitasking and digital stress among adolescents. Heliyon. 2024;10:e31051. doi: 10.1016/j.heliyon.2024.e31051.10.1016/j.heliyon.2024.e31051PMC1112885638803873Stadin M., Nordin M., Broström A., Magnusson Hanson L., Westerlund H., Fransson E. ICT demands and technostress in SLOSH. Comput. Hum. Behav. 2021;114:106486. doi: 10.1016/j.chb.2020.106486.10.1016/j.chb.2020.106486Ninaus K., Diehl S., Terlutter R. Employee perceptions of ICT in work life, perceived burnout, job satisfaction and the role of work-family balance. J. Bus. Res. 2021;136:652–666.. doi: 10.1016/j.jbusres.2021.08.007.10.1016/j.jbusres.2021.08.007Kaltenegger H., Becker L., Rohleder N., Nowak D., Quartucci C., Weigl M. Associations of technostressors at work with burnout symptoms and chronic low-grade inflammation: A cross-sectional analysis in hospital employees. Int. Arch. Occup. Environ. Health. 2023;96:839–856.. doi: 10.1007/s00420-023-01967-8.10.1007/s00420-023-01967-8PMC1016329537148328Salmela-Aro K., Upadyaya K. Role of demands-resources in work engagement and burnout in different career stages. J. Vocat. Behav. 2018;108:190–200.. doi: 10.1016/j.jvb.2018.08.002.10.1016/j.jvb.2018.08.002Su L., Fu S., Qin Y., Li B. IT identity and productivity in SMEs. Int. J. Hosp. Manag. 2024;120:103784. doi: 10.1016/j.ijhm.2024.103784.10.1016/j.ijhm.2024.103784Khalequzzaman M., Wang S., Zhang N., Wang L. Digital transformation stressors in banking: Technostress as a mediator and organizational support as a moderator of job performance. Acta Psychol. 2025;260:105612. doi: 10.1016/j.actpsy.2025.105612.10.1016/j.actpsy.2025.10561240991969Gnugesser E., Jöllenbeck M., Schlenger W., Ochsmann E. Navigating the digital shift: Working conditions and employee health in digital social care. J. Innov. Knowl. 2025;10:100706. doi: 10.1016/j.jik.2025.100706.10.1016/j.jik.2025.100706Sevic A., Foldnes N., Brønnick K. Measuring digital stress in Norway: Translation and validation of the Digital Stressors Scale. Front. Psychol. 2024;15:1297194. doi: 10.3389/fpsyg.2024.1297194.10.3389/fpsyg.2024.1297194PMC1088427138406303Qiu S., Zhang L., You F., Zhao X. Unpacking media channel effects on AI perception: Overload, literacy, and anxiety among Chinese users. Comput. Hum. Behav. 2025;173:108790. doi: 10.1016/j.chb.2025.108790.10.1016/j.chb.2025.108790Alkhayyal S., Bajaba S. Countering technostress in virtual work environments: The role of work-based learning and digital leadership in enhancing employee well-being. Acta Psychol. 2024;248:104377. doi: 10.1016/j.actpsy.2024.104377.10.1016/j.actpsy.2024.10437738941915Gabbiadini A., Paganin G., Simbula S. Teaching after the pandemic: The role of technostress and organizational support on intentions to adopt remote teaching technologies. Acta Psychol. 2023;236:103936. doi: 10.1016/j.actpsy.2023.103936.10.1016/j.actpsy.2023.103936PMC1017463737178653Sende C.C., Soucek R., Ebner K. Coping with digital demands and recovery at work. Comput. Hum. Behav. Rep. 2025;18:100619. doi: 10.1016/j.chbr.2025.100619.10.1016/j.chbr.2025.100619Rickard G., Lenthall S., Dollard M., Opie T., Knight S., Dunn S., Wakerman J., MacLeod M., Seiler J., Brewster-Webb D. Organisational intervention to reduce occupational stress and turnover in hospital nurses in the Northern Territory, Australia. Collegian. 2012;19:211–221. doi: 10.1016/j.colegn.2012.07.001.10.1016/j.colegn.2012.07.00123362607Yeves J., Bargsted M., Torres-Ochoa C. Work schedule flexibility and teleworking were not good together during COVID-19 when testing their effects on work overload and mental health. Front. Psychol. 2022;13:998977. doi: 10.3389/fpsyg.2022.998977.10.3389/fpsyg.2022.998977PMC965038236389483Marsh E., Vallejos E., Spence A. The digital workplace and its dark side: An integrative review. Comput. Hum. Behav. 2022;128:107118. doi: 10.1016/j.chb.2021.107118.10.1016/j.chb.2021.107118Xia N., Ding S., Yuan J. The impact of a challenging work environment: Do job stressors benefit citizenship behavior of project managers. Int. J. Proj. Manag. 2022;40:205–217. doi: 10.1016/j.ijproman.2022.01.007.10.1016/j.ijproman.2022.01.007Koob C., Schröpfer K., Coenen M., Kus S., Schmidt N. Factors influencing study engagement during the COVID-19 pandemic: A cross-sectional study among health and social professions students. PLoS ONE. 2021;16:e0255191. doi: 10.1371/journal.pone.0255191.10.1371/journal.pone.0255191PMC831553634314450Ramstedt Stadin M., Asplund S., Nyman T., Svartengren M., Hellman T. Digital competence is a must: Manager and safety representative perspectives on evolving job skills and balancing digital work environments in transportation, logistics, and home care. Comput. Hum. Behav. Rep. 2024;16:100486. doi: 10.1016/j.chbr.2024.100486.10.1016/j.chbr.2024.100486Dewe P. Encyclopedia of Applied Psychology. Academic Press; Amsterdam, The Netherlands: 2004. Job Stress and Burnout.10.1016/B0-12-657410-3/00442-6Jung J., Brown T., Zablah A. How customer requests influence frontline employee job outcomes: The role of personal appraisal tendencies and situational customer demandingness. J. Retail. 2022;98:315–334. doi: 10.1016/j.jretai.2021.03.005.10.1016/j.jretai.2021.03.005Baseman J., Revere D., Painter I., Stangenes S., Lilly M., Beaton R., Calhoun R., Meischke H. Impact of new technologies on stress, attrition and well-being in emergency call centers: The NextGeneration 9–1–1 study protocol. BMC Public Health. 2018;18:597. doi: 10.1186/s12889-018-5510-x.10.1186/s12889-018-5510-xPMC593603229728094Curbow B., Spratt K., Ungaretti A., McDonnell K., Breckler S. Development of the child care worker job stress inventory. Early Child. Res. Q. 2001;15:515–536. doi: 10.1016/S0885-2006(01)00068-0.10.1016/S0885-2006(01)00068-0Hellhammer D.H., Stone A.A., Hellhammer J., Broderick J. Encyclopedia of Behavioral Neuroscience. Elsevier; Amsterdam, The Netherlands: 2010. Measuring stress.10.1016/B978-0-08-045396-5.00188-3Wekenborg M., Harzbecker J., Bechmann N., Sbiera S., Gilbert S., Schwerdtfeger A. Effects of digital transformation on stress relevant working conditions and psychophysiological stress. Comput. Hum. Behav. Rep. 2025;20:100839. doi: 10.1016/j.chbr.2025.100839.10.1016/j.chbr.2025.100839Almourad M., Alrobai A., Skinner T., Hussain M., Ali R. Digital wellbeing tools through users lens. Technol. Soc. 2021;67:101778. doi: 10.1016/j.techsoc.2021.101778.10.1016/j.techsoc.2021.101778Becker L., Kaltenegger H., Nowak D., Weigl M., Rohleder N. Physiological stress in response to multitasking and work interruptions: Study protocol. PLoS ONE. 2022;17:e0263785. doi: 10.1371/journal.pone.0263785.10.1371/journal.pone.0263785PMC882435435134093Snyder H. Literature review as a research methodology: An overview and guidelines. J. Bus. Res. 2019;104:333–339. doi: 10.1016/j.jbusres.2019.07.039.10.1016/j.jbusres.2019.07.039Grant M.J., Booth A. A typology of reviews: An analysis of 14 review types and associated methodologies. Health Inf. Libr. J. 2009;26:91–108. doi: 10.1111/j.1471-1842.2009.00848.x.10.1111/j.1471-1842.2009.00848.x19490148Paul J., Lim W.M., O’Cass A., Hao A.W., Bresciani S. Scientific procedures and rationales for systematic literature reviews (SLRs) Int. J. Consum. Stud. 2021;45:O1–O16. doi: 10.1111/ijcs.12695.10.1111/ijcs.12695Fekete J.T., Győrffy B. MetaAnalysisOnline.com: Web-Based Tool for the Rapid Meta-Analysis of Clinical and Epidemiological Studies. J. Med. Internet Res. 2025;27:e64016. doi: 10.2196/64016.10.2196/64016PMC1192643839928123Blondel V.D., Guillaume J.L., Lambiotte R., Lefebvre E. Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008;2008:P10008. doi: 10.1088/1742-5468/2008/10/P10008.10.1088/1742-5468/2008/10/P10008Cobo M.J., López-Herrera A.G., Herrera-Viedma E., Herrera F. Science mapping software tools: Review, analysis, and cooperative study among tools. J. Am. Soc. Inf. Sci. Technol. 2011;62:1382–1402. doi: 10.1002/asi.21525.10.1002/asi.21525Page M.J., McKenzie J.E., Bossuyt P.M., Boutron I., Hoffmann T.C., Mulrow C.D., Shamseer L., Tetzlaff J.M., Akl E.A., Brennan S.E., et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ Clin. Res. Ed. 2021;372:n71. doi: 10.1136/bmj.n71.10.1136/bmj.n71PMC800592433782057Krägeloh C.U., Medvedev O.N., Alyami H., Alammar H.A., Hamdan-Mansour A., Alyami E., Alsoudi S., Henning M.A., Alyami M.M. Translation and validation of the Arabic version of the Digital Stress Scale (DSS-A) with three Arabic-speaking samples. Middle East Curr. Psychiatry. 2023;30:118. doi: 10.1186/s43045-023-00387-1.10.1186/s43045-023-00387-1Zhang C., Dai B., Lin L. Validation of a Chinese version of the Digital Stress Scale and development of a short form based on item response theory among Chinese college students. Psychol. Res. Behav. Manag. 2023;16:2897–2911. doi: 10.2147/PRBM.S413162.10.2147/PRBM.S413162PMC1040304937547623Xie P., Mu W., Li Y., Li X., Wang Y. The Chinese version of the Digital Stress Scale: Evaluation of psychometric properties. Curr. Psychol. 2023;42:20532–20542. doi: 10.1007/s12144-022-03156-1.10.1007/s12144-022-03156-1Lao C.K., Su J., Wei S., Yu X., Zhou G. Psychometric Validation of the Revised Chinese Digital Stress Scale in College Students. Acta Sci. Nat. Univ. Pekin. 2023;59:1025–1034. doi: 10.13209/j.0479-8023.2023.055.10.13209/j.0479-8023.2023.055Gao C., Jian M., Yuan A. Factor structure and psychometric properties of the Digital Stress Scale in a Chinese college sample. SAGE Open. 2024;14:21582440241247406. doi: 10.1177/21582440241247406.10.1177/21582440241247406Sariçam H., Günaydin N. The adaptation of the Digital Stress Scale for university students to Turkish: A validity and reliability study. Yükseköğretim Derg. 2024;14:11–24. doi: 10.53478/yuksekogretim.1381953.10.53478/yuksekogretim.1381953Khan A., Ilyas U. Urdu adaptation and validation of Multidimensional Digital Stressor Scale. Media Asia. 2024;52:285–300. doi: 10.1080/01296612.2024.2368344.10.1080/01296612.2024.2368344Dorcic T.M., Smojver-Azic S., Bradic S. Social media use: Association with digital stress and anxiety and depression symptoms in youth. Psihol. Teme. 2024;33:133–154. doi: 10.31820/pt.33.1.7.10.31820/pt.33.1.7Schlenger W., Joellenbeck M., Ochsmann E. Digitalization, technostress, and strain: A longitudinal field study. JMIR Form. Res. 2025;9:e65766. doi: 10.2196/65766.10.2196/65766PMC1246679240997322Pisarska A.M., Kryczka A., Castellone D. Digital well-being interventions and outcomes. Sustainability. 2025;17:5124. doi: 10.3390/su17115124.10.3390/su17115124Mitchell G.D., Brunken R.C., Schwaiger M., Donohue B.C., Krivokapich J., Child J.S. Assessment of mitral flow velocity with exercise by an index of stress-induced left ventricular ischemia in coronary artery disease. Am. J. Cardiol. 1988;61:536–540. doi: 10.1016/0002-9149(88)90760-6.10.1016/0002-9149(88)90760-62964190Rinne K., Kelly A., O’Malley E. Proceedings of the 2010 Twenty-Fifth Annual IEEE Applied Power Electronics Conference and Exposition (APEC), Palm Springs, CA, USA, 21–25 February 2010. IEEE; New York, NY, USA: A novel digital single-wire quasi-democratic stress share scheme for paralleled switching converters; pp. 328–335.10.1109/APEC.2010.5433652Wei X.-H., Gao B., Li Q., Xu K.-Y., Liu A., Chai L.-Q. Digital measurement of the stress of KDP crystal. Guangdian Gongcheng Opto-Electron. Eng. 2011;38:52–56. doi: 10.3969/j.issn.1003-501X.2011.12.010.10.3969/j.issn.1003-501X.2011.12.010Manzetti S., Johansson O. Global electromagnetic toxicity and frequency-induced diseases: Theory and short overview. Pathophysiology. 2012;19:185–191. doi: 10.1016/j.pathophys.2012.04.009.10.1016/j.pathophys.2012.04.00922841384van der Vijgh B., Beun R.J., van Rood M., Werkhoven P. GASICA: Generic automated stress induction and control application. Front. Neurosci. 2014;8:400. doi: 10.3389/fnins.2014.00400.10.3389/fnins.2014.00400PMC425911125538554Perumalla S., Al-Fares A., Flusain R., Mulyono R., al-Ammar N., Al-Kandary A., Singh H., Al-Naeimi R., Prasad U., Scheele E., et al. Regional in-situ stress mapping: An initiative for exploration & development of deep gas reservoirs in Kuwait. Soc. Pet. Eng. Conf. Proc. 2014;5:3516–3533.Motz M., Außerlechner U., Holliber M. Compensation of mechanical stress-induced drift of bandgap references with on-chip stress sensor. IEEE Sens. J. 2015;15:5115–5121. doi: 10.1109/JSEN.2015.2433292.10.1109/JSEN.2015.2433292Google Books Ngram Viewer Team Books Ngram Viewer: Release Notes and Documentation. Jul, 2024. [(accessed on 15 January 2026)]. Available online: https://books.google.com/ngrams.Michel J.-B., Shen Y.K., Aiden A.P., Veres A., Gray M.K., Brockman W., The Google Books Team. Pickett J.P., Hoiberg D., Clancy D., et al. Quantitative analysis of culture using millions of digitized books. Science. 2011;331:176–182. doi: 10.1126/science.1199644.10.1126/science.1199644PMC327974221163965Argyriadi A., Katsarou D., Patelarou A., Megari K., Patelarou E., Kotrotsiou S., Giakoumidakis K., Abdoola S., Mantsos E., Efthymiou E., et al. Digital Stress Scale (DSC): Development and psychometric validation of a measure of stress in the digital age. Int. J. Environ. Res. Public Health. 2025;22:1080. doi: 10.3390/ijerph22071080.10.3390/ijerph22071080PMC1229516440724147Almakrob A.Y., Alduais A. Depression and Anxiety in the Saudi Population: Epidemiological Profiles from Health Surveys and Mental Health Services. Inq. J. Health Care Organ. Provis. Financ. 2025;62 doi: 10.1177/00469580251382027.10.1177/00469580251382027PMC1253620241098088Vignapiano A., Monaco F., Panarello E., Landi S., Di Gruttola B., Malvone R., Martiadis V., Raffone F., Marenna A., Pontillo M., et al. Digital interventions for the rehabilitation of first-episode psychosis: An integrated perspective. Brain Sci. 2025;15:80. doi: 10.3390/brainsci15010080.10.3390/brainsci15010080PMC11763830398514474189717920260328202603302227-90321462026Mar12Healthcare (Basel, Switzerland)Healthcare (Basel)Does Character Strength Have an Influence on Children's Susceptibility to Technological Addiction? A Systematic Review.72410.3390/healthcare14060724Background: The misuse of technology and the consequences of addiction are topics that are increasingly addressed by researchers. Specifically, more attention is being paid to strategies aimed at preventing such addiction in minors. In all likelihood, this is the first systematic review to focus on the role of moral values in preventing minors from developing digital addictions. The aim of the study is to review the scientific research on the issue in order to answer the following questions: (1) Is character strength (or related variables) linked to an increased probability that minors will develop a technology addiction? (2) What types of character strengths protect minors from these problems? (3) What kinds of training programmes have been provided to instil these values in both minors and parents? Methods: The authors systematically examined the peer-reviewed literature from the Web of Science Core Collection (WOS), Medline, and Scopus while adhering to the PRISMA statement. Only articles published until 26 April 2025, and which were written in the English language, were reviewed. The search was conducted with no year restrictions. Results: A total of 609 studies were obtained, of which 9 were finally selected. The results provide a complete overview of the presence of moral values as a protective factor against digital addiction. Conclusions: Several challenges remain with regard to enhancing the knowledge of moral values, as well as their importance as an instrument available to minors for their safe interaction with technology.Jimenez-PerianesAnaAPsycho-Technology Lab, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Spain.Departamento de Psicología y Pedagogía, Facultad de Medicina, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Spain.Instituto Universitario de Estudios de las Adicciones (IEA-CEU), Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Spain.Monfort-VinuesaCarlosC0000-0002-0621-8321Psycho-Technology Lab, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Spain.Departamento de Psicología y Pedagogía, Facultad de Medicina, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Spain.Servicio de Medicina Interna, Hospital HM, 28015 Madrid, Spain.Saiz-ClarElenaEDepartamento Interfacultativo de Matemática Aplicada y Estadística, Facultad de Ciencias Económicas y Empresariales, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Spain.Egea-RomeroMaria PMP0000-0002-4140-8585Psycho-Technology Lab, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Spain.Departamento de Psicología y Pedagogía, Facultad de Medicina, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Spain.RebateCristinaCPsycho-Technology Lab, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Spain.Rodriguez-CañasMonicaMPsycho-Technology Lab, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Spain.VillarroelCarolineCPsycho-Technology Lab, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Spain.RinconEstherE0000-0002-7932-4643Psycho-Technology Lab, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Spain.Departamento de Psicología y Pedagogía, Facultad de Medicina, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Spain.Instituto Universitario de Estudios de las Adicciones (IEA-CEU), Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Spain.engN/AFundación Universitaria San Pablo-CEU (CEU Universities).Journal ArticleReview20260312SwitzerlandHealthcare (Basel)1016665252227-9032character strengthschildrendigital addictionparentstechnologyThe authors declare that there is no conflict of interest in any part of this article.20251217202621620262252026328640202632863920263281172026312epublish41897179PMC1302701410.3390/healthcare14060724healthcare14060724Müller K.W., Ammerschläger M., Freisleder F.J., Beutel M.E., Wölfling K. Suchtartige Internetnutzung als komorbide Störung im jugendpsychiatrischen Setting. Z. Kinder Jugendpsychiatr. Psychother. 2012;40:331–339. doi: 10.1024/1422-4917/a000190.10.1024/1422-4917/a00019022869227Pekonidi A.V. Adicción digital en niños y adolescentes. Estrategias modernas de diagnóstico y tratamiento. RMZh. Revis. Méd. 2021;5:322–329. doi: 10.32364/2587-6821-2021-5-5-322-329.10.32364/2587-6821-2021-5-5-322-329Purificacion B., Julyn A., Vallespin M.R. Understanding the multifaceted impacts of social media addiction on minors: A comprehensive analysis of psychological, behavioral, and physiological dimensions. [(accessed on 1 January 2025)];Int. J. Curr. Sci. Res. Rev. 2024 7:2604–2611. doi: 10.47191/ijcsrr/V7-i5-20. Available online: https://ssrn.com/abstract=4835031.10.47191/ijcsrr/V7-i5-20Young K.S. Internet addiction: The emergence of a new clinical disorder. CyberPsychology Behav. 1998;1:237–244. doi: 10.1089/cpb.1998.1.237.10.1089/cpb.1998.1.237Baumgartner S.E., Parry D.A., Beyens I., Wiradhany W., Uncapher M., Wagner A.D., Bavelier D. Handbook of Children and Screens. Springer; Cham, Switzerland: 2025. The Short- and Long-Term Effects of Digital Media Use on Attention.10.1007/978-3-031-69362-5_5Dore R.A., Jing M., Taylor G., Madigan S., Samudra P.G., Sundqvist A.S., Xu Y. Handbook of Children and Screens. Springer; Cham, Switzerland: 2025. Digital Media Use and Language Development in Early Childhood.10.1007/978-3-031-69362-5_6Richert R.A., Choi K., Gleason T.R., Goldstein T.R., Sibert S.M. Handbook of Children and Screens. Springer; Cham, Switzerland: 2025. Imagination, Creativity, and Play.10.1007/978-3-031-69362-5_7Tremblay M.S., Kuzik N., Biddle S.J.H., Carson V., Chinapaw M.J.M., Dumuid D., Huang W.Y., Saunders T.J., Staiano A.E., Pate R.R. Handbook of Children and Screens. Springer; Cham, Switzerland: 2025. Digital Screen Media Use, Movement Behaviors, and Child Health.10.1007/978-3-031-69362-5_10Rincón E., Andrade Pino P., editors. Adicciones Digitales en Menores: Fundamentos Para Educadores y Sanitarios. Ediciones Pirámide; Madrid, Spain: 2024.Vilić V. Users’ considerations about possibilities of self-protection on social networks. Open J. Leg. Stud. 2018;1:9–24. doi: 10.32591/coas.ojls.0101.02009v.10.32591/coas.ojls.0101.02009vGiménez Hernández M. Ph.D. Thesis. Universidad Complutense de Madrid; Madrid, Spain: 2009. La Medida de las Fortalezas Psicológicas en Adolescentes (VIA-Youth): Relación con Clima Familiar, Psicopatología y Bienestar Psicológico.Dehghani M., Johnson K., Hoover J., Sagi E., Garten J., Parmar N.J., Vaisey S., Iliev R., Graham J. Purity homophily in social networks. J. Exp. Psychol. Gen. 2016;145:366–375. doi: 10.1037/xge0000139.10.1037/xge000013926726910Peterson C., Seligman M.E.P. Character Strengths and Virtues. A Handbook and Classification. Oxford University Press; Oxford, UK: American Psychological Association; Washington, DC, USA: 2004.Gentry B., Cullen K., Altman D. The Irony of Integrity: A Study of the Character Strengths of Leaders [White Paper] Center for Creative Leadership; Greensboro, NC, USA: 2016.10.35613/ccl.2016.1032Ben-Nun Bloom P., Levitan L.C. We’re closer than I thought: Social network heterogeneity, morality, and political persuasion. Polit. Psychol. 2011;32:643–665. doi: 10.1111/j.1467-9221.2011.00826.x.10.1111/j.1467-9221.2011.00826.xArrivillaga C., Rey L., Extremera N. The chain-mediating role of perceived stress and problematic smartphone use in the link between critical thinking and academic engagement among Spanish adolescents. Cyberpsychol. J. Psychosoc. Res. Cyberspace. 2022;16:7. doi: 10.5817/CP2022-4-7.10.5817/CP2022-4-7Horstink T.S. Master’s Thesis. University of Twente; Enschede, The Netherlands: 2011. The Effects of Social Networks on Group Moral Reasoning in the Royal Netherlands Army.Scott E.D. Moral values: Situationally defined individual differences. Bus. Ethics Q. 2000;10:497–520. doi: 10.2307/3857888.10.2307/3857888Scott E.D. Organizational moral values. Bus. Ethics Q. 2002;12:33–55. doi: 10.2307/3857647.10.2307/3857647Milchram C., Van de Kaa G., Doorn N., Künneke R. Moral values as factors for social acceptance of smart grid technologies. Sustainability. 2018;10:2703. doi: 10.3390/su10082703.10.3390/su10082703Janic Z., Maykova V., Maykova N., Ilchenko D. Spiritual and moral personality development as a factor society safe existence; Proceedings of the 2nd International Conference on Contemporary Education, Social Sciences and Ecological Studies (CESSES 2019); Moscow, Russia. 5–6 June 2019;10.2991/cesses-19.2019.266Jeffries F.L. Predicting safety related attitudes in the workplace: The influence of moral maturity and emotional intelligence. J. Behav. Appl. Manag. 2011;12:200–216. doi: 10.21818/001c.17862.10.21818/001c.17862Mayerson N.H. The character strengths response: An urgent call to action. Front. Psychol. 2020;11:2106. doi: 10.3389/fpsyg.2020.02106.10.3389/fpsyg.2020.02106PMC747295332973633Page M.J., McKenzie J.E., Bossuyt P.M., Boutron I., Hoffmann T.C., Mulrow C.D., Shamseer L., Tetzlaff J.M., Akl E.A., Brennan S.E., et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. doi: 10.1136/bmj.n71.10.1136/bmj.n71PMC800592433782057Booth A., Clarke M., Dooley G., Ghersi D., Moher D., Petticrew M., Stewart L. The nuts and bolts of PROSPERO: An international prospective register of systematic reviews. Syst. Rev. 2012;1:2. doi: 10.1186/2046-4053-1-2.10.1186/2046-4053-1-2PMC334867322587842Moher D., Shamseer L., Clarke M., Ghersi D., Liberati A., Petticrew M., Shekelle P., Stewart L.A., PRISMA-P Group Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst. Rev. 2015;4:1. doi: 10.1186/2046-4053-4-1.10.1186/2046-4053-4-1PMC432044025554246Page M.J., McKenzie J.E., Bossuyt P.M., Boutron I., Hoffmann T.C., Mulrow C.D., Shamseer L., Tetzlaff J.M., Akl E.A., Brennan S.E., et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Int. J. Surg. 2021;88:105906. doi: 10.1016/j.ijsu.2021.105906.10.1016/j.ijsu.2021.10590633789826Shamseer L., Moher D., Clarke M., Ghersi D., Liberati A., Petticrew M., Shekelle P., Stewart L.A., The PRISMA-P Group Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: Elaboration and explanation. BMJ. 2015;350:g7647. doi: 10.1136/bmj.g7647.10.1136/bmj.g764725555855Altman D.G. Practical Statistics for Medical Research. CRC Press; Boca Raton, FL, USA: 1990.Pluye P., Gagnon M., Griffiths F., Johnson-Lafleur J. A scoring system for appraising mixed methods research, and concomitantly appraising qualitative, quantitative and mixed methods primary studies in mixed studies reviews. Int. J. Nurs. Stud. 2009;46:529–546. doi: 10.1016/j.ijnurstu.2009.01.009.10.1016/j.ijnurstu.2009.01.00919233357Hong Q.N., Pluye P., Fàbregues S., Bartlett G., Boardman F., Cargo M., Dagenais P., Gagnon M.-P., Griffiths F., Nicolau B., et al. Mixed Methods Appraisal Tool (MMAT) McGill University; Montreal, QC, Canada: 2018. Version 2018.Aral A., Usta M.B. Intrapersonal and family predictors of internet addiction in attention deficit hyperactivity disorder. J. Gambl. Issues. 2022;50:98–131. doi: 10.4309/XHMA4001.10.4309/XHMA4001Ayten A., Acat B. Internet addiction, values, and religiosity as predictors of life satisfaction among adolescents: A quantitative study on high school students. Addicta Turk. J. Addict. 2019;6:847–870. doi: 10.15805/addicta.2019.6.3.0091.10.15805/addicta.2019.6.3.0091Bilge M., Uçan G., Baydur H. Investigating the association between adolescent internet addiction and parental attitudes. Int. J. Public Health. 2022;67:1605065. doi: 10.3389/ijph.2022.1605065.10.3389/ijph.2022.1605065PMC958951536299409Cacioppo M., Barni D., Correale C., Mangialavori S., Danioni F., Gori A. Do attachment styles and family functioning predict adolescents’ problematic internet use? A relative weight analysis. J. Child Fam. Stud. 2019;28:1263–1271. doi: 10.1007/s10826-019-01357-0.10.1007/s10826-019-01357-0Casaló L.V., Escario J.J. Predictors of excessive internet use among adolescents in Spain: The relevance of the relationship between parents and their children. Comput. Human. Behav. 2019;92:344–351. doi: 10.1016/j.chb.2018.11.042.10.1016/j.chb.2018.11.042Chen Y.-L., Chen S.-H., Gau S.S.-F. ADHD and autistic traits, family function, parenting style, and social adjustment for Internet addiction among children and adolescents in Taiwan: A longitudinal study. Res Dev Disabil. 2015;39:20–31. doi: 10.1016/j.ridd.2014.12.025.10.1016/j.ridd.2014.12.02525617844Ding W.-N., Sun J.-H., Sun Y.-W., Chen X., Zhou Y., Zhuang Z.-G., Li L., Zhang Y., Xu J.-R., Du Y.-S. Trait impulsivity and impaired prefrontal impulse inhibition function in adolescents with internet gaming addiction revealed by a Go/No-Go fMRI study. Behav. Brain Funct. 2014;10:20. doi: 10.1186/1744-9081-10-20.10.1186/1744-9081-10-20PMC405041224885073Fomichov V.A., Fomichova O.S. A contribution of cognitonics to secure living in information society. Inform. Int. J. Comput. Inform. 2012;36:121–130.Ge Y., Se J., Zhang J. Research on relationship among internet-addiction, personality traits and mental health of urban left-behind children. Glob. J. Health Sci. 2015;7:60. doi: 10.5539/gjhs.v7n4p60.10.5539/gjhs.v7n4p60PMC480214925946911Juthamanee S., Gunawan J. Factors related to internet and game addiction among adolescents: A scoping review. Belitung Nurs. J. 2021;7:62–71. doi: 10.33546/bnj.1192.10.33546/bnj.1192PMC1035365037469945Kaya A., Aydemir K., İşler A. A controversial issue in adolescents: Is the effect of digital games on creative personality traits positive or negative? Arch. Psychiatr. Nurs. 2024;49:67–72. doi: 10.1016/j.apnu.2024.02.001.10.1016/j.apnu.2024.02.00138734457Kuss D.J., Van Rooij A.J., Shorter G.W., Griffiths M.D., Van De Mheen D. Internet addiction in adolescents: Prevalence and risk factors. Comput. Hum. Behav. 2013;29:1987–1996. doi: 10.1016/j.chb.2013.04.002.10.1016/j.chb.2013.04.002Lee J.Y., Ban D., Kim S.Y., Kim J.M., Shin I.S., Yoon J.S., Kim S.W. Negative life events and problematic internet use as factors associated with psychotic-like experiences in adolescents. Front. Psychiatry. 2019;10:369. doi: 10.3389/fpsyt.2019.00369.10.3389/fpsyt.2019.00369PMC654919331191372Lee J.Y., Park E.J., Kwon M., Choi J.H., Jeong J.E., Choi J.S., Choi S.W., Lee C.U., Kim D.J. The difference in comorbidities and behavioral aspects between internet abuse and internet dependence in Korean male adolescents. Psychiatry Investig. 2014;11:387–393. doi: 10.4306/pi.2014.11.4.387.10.4306/pi.2014.11.4.387PMC422520225395969Li D., Xu Y., Cao S. How does trait mindfulness weaken the effects of risk factors for adolescent smartphone addiction? A moderated mediation model. Behav. Sci. 2023;13:540. doi: 10.3390/bs13070540.10.3390/bs13070540PMC1037638637503987Malaeb D., Awad E., Haddad C., Sacre H., Khansa W., Ammar J., Ghosn J., Richa S., Hallit S., Obeid S. Bullying victimization among Lebanese adolescents: The role of child abuse, internet addiction, social phobia and depression and validation of the Illinois Bully Scale. BMC Pediatr. 2020;20:520. doi: 10.1186/s12887-020-02413-1.10.1186/s12887-020-02413-1PMC766650633187493Mastrobattista L., Perez L.J.G., Gallimberti L., Genetti B., Andreotti A., Fassinato D., Monacis L., Anselmi P., Colledani D., Minutillo A., et al. Psychosocial risk and protective factors for youth problem behavior are associated with food addiction in the Generation Z. Front. Public Health. 2024;12:1414110. doi: 10.3389/fpubh.2024.1414110.10.3389/fpubh.2024.1414110PMC1116311738859893Mishra J., Behera M.R., Mitra R., Samanta P., Mahapatra P.K., Kar S. Prevalence and impact of internet addiction disorder among adolescents and young adults. Open Public Health J. 2024;17 doi: 10.2174/0118749445345806241010081642.10.2174/0118749445345806241010081642Müller M. Educational standards in the school curriculum and the role of the mass media: The case of Croatia. Ann. Univ. Apulensis Ser. Historica. 2017;28:159–174. doi: 10.19233/ASHS.2017.13.10.19233/ASHS.2017.13Munno D., Saroldi M., Bechon E., Sterpone S.C., Zullo G. Addictive behaviors and personality traits in adolescents. CNS Spectr. 2016;21:207–213. doi: 10.1017/S1092852915000474.10.1017/S109285291500047426268304Peris M., de la Barrera U., Schoeps K., Montoya-Castilla I. Psychological risk factors that predict social networking and internet addiction in adolescents. Int. J. Environ. Res. Public Health. 2020;17:4598. doi: 10.3390/ijerph17124598.10.3390/ijerph17124598PMC734461332604834Riany Y.E., Saadatunnisa R. Adolescent character strengths: Associations with parental attachment and internet addiction. Int. J. Interdiscip. Educ. Stud. 2022;17:71–84. doi: 10.18848/2327-011X/CGP/v17i01/71-84.10.18848/2327-011X/CGP/v17i01/71-84Satria B., Oktari R.S. The Relationship between Emotional Intelligence and Internet Game Addiction among Adolescents in Aceh Indonesia. J. Liaquat Univ. Med. Health Sci. 2024;23:7–11. doi: 10.22442/jlumhs.2024.01118.10.22442/jlumhs.2024.01118Sun Y., Wilkinson J.S. Parenting style, personality traits, and interpersonal relationships: A model of prediction of internet addiction. Int. J. Commun. 2020;14:2163–2185.Trumello C., Babore A., Candelori C., Morelli M., Bianchi D. Relationship with parents, emotion regulation, and callous-unemotional traits in adolescents’ internet addiction. Biomed. Res. Int. 2018;2018:7914261. doi: 10.1155/2018/7914261.10.1155/2018/7914261PMC598928729951544Xiuqin H., Huimin Z., Mengchen L., Jinan W., Ying Z., Ran T. Mental health, personality, and parental rearing styles of adolescents with internet addiction disorder. Cyberpsychol Behav. Soc. Netw. 2010;13:401–406. doi: 10.1089/cyber.2009.0222.10.1089/cyber.2009.022220712498Yan W., Li Y., Sui N. The relationship between recent stressful life events, personality traits, perceived family functioning and internet addiction among college students. Stress Health. 2014;30:3–11. doi: 10.1002/smi.2490.10.1002/smi.249023616371Bao K., Zhang X., Cai L. The closed loop between parental upbringing and online game addiction: A narrative study of rural children’s growth in China. Psychol. Res. Behav. Manag. 2024;17:1703–1716. doi: 10.2147/PRBM.S457068.10.2147/PRBM.S457068PMC1104247838660459Chen I.-H., Lee Z.-H., Dong X., Gamble J.H., Feng H. The influence of parenting style and time management tendency on internet gaming disorder among adolescents in Taiwan: A mediation model. Int. J. Environ. Res. Public Health. 2020;17:9120. doi: 10.3390/ijerph17239120.10.3390/ijerph17239120PMC773053033291336Dalvi-Esfahani M., Niknafs A., Alaedini Z., Ahmadabadi H.B., Kuss D.J., Ramayah T. Social media addiction and empathy: The moderating impact of personality traits among high school students. Telemat. Inform. 2021;57:101516. doi: 10.1016/j.tele.2020.101516.10.1016/j.tele.2020.101516Hayixibayi A., Strodl E., Chen W., Kelly A.B. Associations between adolescent problematic internet use and relationship problems in Chinese families: Findings from a large-scale survey. JMIR Pediatr. Parent. 2022;5:e35240. doi: 10.2196/35240.10.2196/35240PMC964151736279160Lee S.Y., Lee H.K., Jeong E.J. Effects of parental psychological control on children’s school life: Mobile phone dependency as mediator. Child. Indic. Res. 2016;9:417–435. doi: 10.1007/s10826-015-0251-2.10.1007/s10826-015-0251-2Martins M.V., Formiga A., Santos C., Sousa D., Resende C., Campos R., Nogueira N., Carvalho P., Ferreira S. Adolescent internet addiction—Role of parental control and adolescent behaviours. Int. J. Pediatr. Adolesc. Med. 2020;7:116–120. doi: 10.1016/j.ijpam.2019.12.003.10.1016/j.ijpam.2019.12.003PMC756807033094139Monteiro F., Simões M., Relva I.C. Internet addiction, sleep habits and family communication: The perspectives of a sample of adolescents. Healthcare. 2023;11:3194. doi: 10.3390/healthcare11243194.10.3390/healthcare11243194PMC1074262538132084Nwufo I.J., Ike O.O. Personality traits and internet addiction among adolescent students: The moderating role of family functioning. Int. J. Environ. Res. Public Health. 2024;21:520. doi: 10.3390/ijerph21050520.10.3390/ijerph21050520PMC1112127538791735Qiu C., Qi Y., Yin Y. Multiple Intermediary Model Test of Adolescent Physical Exercise and Internet Addiction. Int. J. Environ. Res. Public Health. 2023;20:4030. doi: 10.3390/ijerph20054030.10.3390/ijerph20054030PMC1000228136901042Jiménez-Perianes A. ¿Cómo se lo podemos explicar a nuestros hijos? In: Hiperconectados ¿Abusan Nuestros Hijos del Móvil e Internet? Pirámide; Madrid, Spain: 2025. pp. 111–120.Bandura A. Self-Efficacy: The Exercise of Control. W. H. Freeman; New York, NY, USA: 1997.Eisenberg N., Spinrad T.L., Sadovsky A. Empathy-related responding in children. In: Killen M., Smetana J.G., editors. Handbook of Moral Development. Lawrence Erlbaum Associates Publishers; Mahwah, NJ, USA: 2006. pp. 517–549.Rincon E., Pineda-Ràfols A., Pérez M., San-Martín-Suárez J., Egea-Romero M.P. Are parent training programs useful for reducing problematic or addictive use of technology in children? A systematic review. Comput. Hum. Behav. Rep. 2025;18:100672. doi: 10.1016/j.chbr.2025.100672.10.1016/j.chbr.2025.100672Zimmerman B.J. Becoming a self-regulated learner: An overview. Theory Pract. 2002;41:64–70. doi: 10.1207/s15430421tip4102_2.10.1207/s15430421tip4102_2Tyler T.R., Blader S.L. The Group Engagement Model: Procedural Justice, Social Identity, and Cooperative Behavior. Pers. Soc. Psychol. Rev. 2003;7:349–361. doi: 10.1207/S15327957PSPR0704_07.10.1207/S15327957PSPR0704_0714633471Lubans D., Richards J., Hillman C., Faulkner G., Beauchamp M., Nilsson M., Kelly P., Smith J., Raine L., Biddle S. Physical activity for cognitive and mental health in youth: A systematic review of mechanisms. Pediatrics. 2016;138:e20161642. doi: 10.1542/peds.2016-1642.10.1542/peds.2016-164227542849Lavy S. A review of character strengths interventions in twenty-first-century schools: Their importance and how they can be fostered. Appl. Res. Qual. Life. 2020;15:573–596. doi: 10.1007/s11482-018-9700-6.10.1007/s11482-018-9700-6Kim S.M., Park J., Lee H.K. Internet Gaming Disorder linked to weaker emotion regulation and response inhibition. Addiction. 2023;118:601–602. doi: 10.1111/add.16089.10.1111/add.160893647860341895658202603292210-741X5052026Mar26Clinics and research in hepatology and gastroenterologyClin Res Hepatol GastroenterolPredictive utility of the C-reactive protein-albumin-lymphocyte (CALLY) index in Colorectal cancer: A meta-analysis.10281610281610.1016/j.clinre.2026.102816S2210-7401(26)00063-XThe prognostic performance of the C-reactive protein-albumin-lymphocyte (CALLY) index in individuals with colorectal cancer (CRC) remains inconsistent. Therefore, this research intended to ascertain the utility of CALLY for prognostic prediction in individuals with CRC.PubMed, Cochrane Library (Cochrane), Web of Science (WOS), and Embase were retrieved to collect articles published up to October 20, 2025, ascertaining the prognostic utility of CALLY in CRC. The overall hazard ratio (HR) and its 95% confidence interval (CI) were estimated to appraise the relations between CALLY and overall survival (OS), disease-free survival (DFS) or progression-free survival (PFS), and recurrence-free survival (RFS). All analyses were implemented utilizing STATA 15.1.Totally 10 studies involving 7,467 individuals with CRC were included. Analysis indicated that, with low CALLY as a reference, high CALLY was a substantial predictor of good prognoses for OS (HR=0.58, 95% CI: 0.52-0.63, p<0.001), DFS/PFS (HR=0.39, 95% CI: 0.23-0.66, p<0.001), and RFS (HR=0.70, 95% CI: 0.63-0.79, p<0.001). Subgroup analysis demonstrated that high CALLY was robust as a predictor of good prognosis. Regional heterogeneity was observed in its prediction of OS, while consistent results were found across subgroups with different analysis methods and sample sizes.In the current research, with low CALLY as a reference, high CALLY was substantially linked to better OS, DFS/PFS, and RFS in individuals with CRC. CALLY may act as a bioindicator for the prognosis of individuals with CRC.Copyright © 2026 Elsevier Masson SAS. All rights reserved.LiXiuxiXSchool of Nursing, Beihua University, Jilin, Jilin 132013, China.ChuTongtongTSchool of Nursing, Beihua University, Jilin, Jilin 132013, China.ChenWenwenWSchool of Nursing, Beihua University, Jilin, Jilin 132013, China.ZhangLijuanLSchool of Nursing, Beihua University, Jilin, Jilin 132013, China. Electronic address: 18604498582@163.com.engJournal ArticleReview20260326FranceClin Res Hepatol Gastroenterol1015536592210-7401IMCALLY indexColorectal cancerMeta-analysisPrognosisDeclaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.20262920263520263252026328042202632804220263272018aheadofprint4189565810.1016/j.clinre.2026.102816S2210-7401(26)00063-X