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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="review-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">Russian Medicine</journal-id><journal-title-group><journal-title xml:lang="en">Russian Medicine</journal-title><trans-title-group xml:lang="ru"><trans-title>Российский медицинский журнал</trans-title></trans-title-group></journal-title-group><issn publication-format="print">0869-2106</issn><issn publication-format="electronic">2412-9100</issn><publisher><publisher-name xml:lang="en">Eco-Vector</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">699956</article-id><article-id pub-id-type="doi">10.17816/medjrf699956</article-id><article-id pub-id-type="edn">NUNQKT</article-id><article-categories><subj-group subj-group-type="toc-heading" xml:lang="en"><subject>Systematic reviews</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="ru"><subject>Систематические обзоры</subject></subj-group><subj-group subj-group-type="article-type"><subject>Review Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Application of biomaterials and artificial intelligence technologies in the prevention and treatment of dental caries in children: a systematic review</article-title><trans-title-group xml:lang="ru"><trans-title>Применение биоматериалов и технологий искусственного интеллекта в профилактике и лечении кариеса у детей: систематический обзор</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-8230-0258</contrib-id><contrib-id contrib-id-type="spin">1029-3420</contrib-id><name-alternatives><name xml:lang="en"><surname>Khromenkova</surname><given-names>Ksenia V.</given-names></name><name xml:lang="ru"><surname>Хроменкова</surname><given-names>Ксения Владимировна</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>MD, Cand. Sci. (Medicine)</p></bio><bio xml:lang="ru"><p>канд. мед. наук</p></bio><email>ksyu_kh20.04@mail.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0007-7012-0947</contrib-id><name-alternatives><name xml:lang="en"><surname>Chekoev</surname><given-names>Azamat A.</given-names></name><name xml:lang="ru"><surname>Чекоев</surname><given-names>Азамат Аланович</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>azamatchekoev@gmail.com</email><xref ref-type="aff" rid="aff2"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0004-8886-7975</contrib-id><name-alternatives><name xml:lang="en"><surname>Dzidzoev</surname><given-names>Artur D.</given-names></name><name xml:lang="ru"><surname>Дзидзоев</surname><given-names>Артур Дударович</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>gavriiltumanov@gmail.com</email><xref ref-type="aff" rid="aff2"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0005-4428-5073</contrib-id><name-alternatives><name xml:lang="en"><surname>Dzavkaev</surname><given-names>Akso A.</given-names></name><name xml:lang="ru"><surname>Дзавкаев</surname><given-names>Аксо Александрович</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>azdavkaev@mail.ru</email><xref ref-type="aff" rid="aff2"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0003-9116-1976</contrib-id><name-alternatives><name xml:lang="en"><surname>Tishchenko</surname><given-names>Marta I.</given-names></name><name xml:lang="ru"><surname>Тищенко</surname><given-names>Марта Ивановна</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>byebyeboy@mail.ru</email><xref ref-type="aff" rid="aff3"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0009-0524-3427</contrib-id><name-alternatives><name xml:lang="en"><surname>Asbieva</surname><given-names>Toita S.</given-names></name><name xml:lang="ru"><surname>Асбиева</surname><given-names>Тоита Султановна</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>asbieva03@bk.ru</email><xref ref-type="aff" rid="aff2"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0001-5765-6479</contrib-id><name-alternatives><name xml:lang="en"><surname>Debieva</surname><given-names>Elina I.</given-names></name><name xml:lang="ru"><surname>Дебиева</surname><given-names>Элина Исаевна</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>elinadebieva@icloud.com</email><xref ref-type="aff" rid="aff2"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0006-0826-7123</contrib-id><name-alternatives><name xml:lang="en"><surname>Ktoian</surname><given-names>Anna A.</given-names></name><name xml:lang="ru"><surname>Ктоян</surname><given-names>Анна Артаваздовна</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>ktoyan2003@mail.ru</email><xref ref-type="aff" rid="aff4"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0001-6599-4267</contrib-id><contrib-id contrib-id-type="spin">1946-0930</contrib-id><name-alternatives><name xml:lang="en"><surname>Gorislova</surname><given-names>Anastasia Yu.</given-names></name><name xml:lang="ru"><surname>Горислова</surname><given-names>Анастасия Юрьевна</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>agorislova@bk.ru</email><xref ref-type="aff" rid="aff4"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Russian Medical Academy of Continuous Professional Education</institution></aff><aff><institution xml:lang="ru">Российская медицинская академия непрерывного профессионального образования</institution></aff></aff-alternatives><aff-alternatives id="aff2"><aff><institution xml:lang="en">North-Ossetian State Medical Academy</institution></aff><aff><institution xml:lang="ru">Северо-Осетинская государственная медицинская академия</institution></aff></aff-alternatives><aff-alternatives id="aff3"><aff><institution xml:lang="en">Kuban State Medical University</institution></aff><aff><institution xml:lang="ru">Кубанский государственный медицинский университет</institution></aff></aff-alternatives><aff-alternatives id="aff4"><aff><institution xml:lang="en">Peoples’ Friendship University of Russia named after Patrice Lumumba</institution></aff><aff><institution xml:lang="ru">Российский университет дружбы народов имени Патриса Лумумбы</institution></aff></aff-alternatives><pub-date date-type="preprint" iso-8601-date="2026-03-05" publication-format="electronic"><day>05</day><month>03</month><year>2026</year></pub-date><pub-date date-type="pub" iso-8601-date="2026-07-13" publication-format="electronic"><day>13</day><month>07</month><year>2026</year></pub-date><volume>32</volume><issue>3</issue><issue-title xml:lang="en"/><issue-title xml:lang="ru"/><fpage>251</fpage><lpage>261</lpage><history><date date-type="received" iso-8601-date="2025-12-29"><day>29</day><month>12</month><year>2025</year></date><date date-type="accepted" iso-8601-date="2026-02-09"><day>09</day><month>02</month><year>2026</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2026, Eco-Vector</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2026, Эко-Вектор</copyright-statement><copyright-year>2026</copyright-year><copyright-holder xml:lang="en">Eco-Vector</copyright-holder><copyright-holder xml:lang="ru">Эко-Вектор</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/" start_date="2029-07-13"/><license><ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/">https://eco-vector.com/for_authors.php#07</ali:license_ref></license></permissions><self-uri xlink:href="https://medjrf.com/0869-2106/article/view/699956">https://medjrf.com/0869-2106/article/view/699956</self-uri><abstract xml:lang="en"><p><bold>BACKGROUND: </bold>Dental caries in children and adolescents remains one of the most prevalent chronic conditions worldwide, affecting quality of life, general health, and social functioning. In recent years, pediatric dentistry has seen the development of two areas: the use of minimally invasive and biocompatible dental biomaterials, and the use of artificial intelligence (AI) for caries detection, risk prediction, and prevention.</p> <p><bold>AIM: </bold>To systematically review clinical studies on the use of dental biomaterials and artificial intelligence technologies in caries prevention and management in children and adolescents, and to evaluate the strength of the available evidence and potential for clinical integration.</p> <p><bold>METHODS: </bold>This systematic review was conducted according to PRISMA 2020 guidelines. Searches were performed in MEDLINE and Google Scholar for studies published between 2020 and 2025. Eligibility criteria were defined using the PICO framework. The analysis included randomized controlled trials, cohort and diagnostic studies, and relevant systematic reviews and meta-analyses. Risk of bias was assessed using RoB 2.0 and ROBINS-I scores, and QUADAS-2 and AMSTAR 2 domains.</p> <p><bold>RESULTS: </bold>Seventeen original clinical studies and four systematic reviews were included in the qualitative synthesis. AI algorithms based on clinical and social data demonstrated moderate diagnostic and prognostic accuracy (AUC approximately 0.75–0.80), comparable to traditional statistical approaches. Biomaterial-based interventions, including atraumatic restorative treatment, Hall technique, silver-containing agents, and calcium-silicate cements, showed high clinical efficacy, particularly within minimally invasive treatment strategies.</p> <p><bold>CONCLUSION: </bold>Modern biomaterials and artificial intelligence technologies show considerable potential for personalized caries prevention and management in children. However, prospective studies with long-term follow-up and standardized outcomes are needed to support their widespread clinical implementation.</p></abstract><trans-abstract xml:lang="ru"><p><bold>Введение. </bold>Кариес у детей и подростков остаётся одной из наиболее распространённых хронических патологий, оказывая влияние на качество жизни, общее здоровье и социальную адаптацию. В последние годы в детской стоматологии активно развиваются два направления: применение биоматериалов, ориентированных на минимально инвазивное лечение и биосовместимость, а также использование технологий искусственного интеллекта для диагностики, прогнозирования и профилактики кариеса.</p> <p><bold>Цель. </bold>Провести систематический обзор клинических исследований, посвящённых применению стоматологических биоматериалов и технологий искусственного интеллекта в профилактике и лечении кариеса у детей и подростков, с оценкой качества доказательств и потенциальных возможностей интеграции данных подходов в клиническую практику.</p> <p><bold>Методы. </bold>Систематический обзор выполнен в соответствии с рекомендациями PRISMA 2020. Поиск литературы проводили в базах MEDLINE и Google Scholar за 2020–2025 гг. Критерии включения определяли по схеме PICO. В анализ включали рандомизированные контролируемые исследования, когортные и диагностические исследования, а также систематические обзоры и метаанализы. Оценку риска систематической ошибки выполняли с использованием шкал RoB 2.0, ROBINS-I, доменов QUADAS-2 и AMSTAR 2.</p> <p><bold>Результаты. </bold>В качественный анализ включено 17 оригинальных клинических исследований и 4 систематических обзора. Алгоритмы искусственного интеллекта на основе клинико-социальных данных демонстрировали умеренную диагностическую и прогностическую точность (площадь под кривой ≈0,75–0,80), сопоставимую с традиционными статистическими моделями. Биоматериальные подходы, включая атравматическую реставрацию, Hall-технику, серебросодержащие материалы и кальций-силикатные цементы, показали высокую клиническую эффективность, особенно при минимально инвазивных стратегиях лечения.</p> <p><bold>Заключение. </bold>Современные биоматериалы и технологии искусственного интеллекта обладают значительным потенциалом для персонализированной профилактики и лечения кариеса у детей. Однако для их широкой клинической интеграции необходимы проспективные исследования с длительным наблюдением и унифицированными исходами.</p></trans-abstract><kwd-group xml:lang="en"><kwd>pediatric dental caries</kwd><kwd>biomaterials</kwd><kwd>artificial intelligence</kwd><kwd>minimally invasive dentistry</kwd><kwd>atraumatic restorative treatment</kwd><kwd>machine learning</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>кариес у детей</kwd><kwd>биоматериалы</kwd><kwd>искусственный интеллект</kwd><kwd>минимально инвазивная стоматология</kwd><kwd>атравматическая реставрация</kwd><kwd>машинное обучение</kwd></kwd-group><funding-group/></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>Fleming E, Afful J. Prevalence of total and untreated dental caries among youth: United States, 2015–2016. NCHS Data Brief. 2018;(307):1–8.</mixed-citation></ref><ref id="B2"><label>2.</label><mixed-citation>Hall-Scullin E, Whitehead H, Milsom K, et al. Longitudinal study of caries development from childhood to adolescence. J Dent Res. 2017;96(7):762–767. doi: 10.1177/0022034517696457</mixed-citation></ref><ref id="B3"><label>3.</label><mixed-citation>Mosaddad SA, Rasoolzade B, Namanloo RA, et al. Stem cells and common biomaterials in dentistry: a review study. J Mater Sci: Mater Med. 2022;33(7):55. doi: 10.1007/s10856-022-06676-1 EDN: VTXUHP</mixed-citation></ref><ref id="B4"><label>4.</label><mixed-citation>de Amorim RG, Frencken JE, Raggio DP, et al. Survival percentages of atraumatic restorative treatment (ART) restorations and sealants in posterior teeth: an updated systematic review and meta-analysis. Clin Oral Invest. 2018;22(8):2703–2725. doi: 10.1007/s00784-018-2625-5 EDN: ZJHYGO</mixed-citation></ref><ref id="B5"><label>5.</label><mixed-citation>Arzani S, Karimi A, Iranmanesh P, et al. Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of imaging modalities: An umbrella review with meta-analysis. PLoS One. 2025;20(8):e0329986. doi: 10.1371/journal.pone.0329986 EDN: ZXXOMK</mixed-citation></ref><ref id="B6"><label>6.</label><mixed-citation>Karhade DS, Roach J, Shrestha P, et al. An automated machine learning classifier for early childhood caries. Pediatr Dent. 2021;43(3):191–197.</mixed-citation></ref><ref id="B7"><label>7.</label><mixed-citation>Park YH, Kim SH, Choi YY. Prediction models of early childhood caries based on machine learning algorithms. IJERPH. 2021;18(16):8613. doi: 10.3390/ijerph18168613 EDN: DXXWPH</mixed-citation></ref><ref id="B8"><label>8.</label><mixed-citation>Ramos-Gomez F, Marcus M, Maida CA, et al. Using a machine learning algorithm to predict the likelihood of presence of dental caries among children aged 2 to 7. Dentistry Journal. 2021;9(12):141. doi: 10.3390/dj9120141 EDN: CNGTAY</mixed-citation></ref><ref id="B9"><label>9.</label><mixed-citation>Wu TT, Xiao J, Sohn MB, et al. Machine learning approach identified multi-platform factors for caries prediction in child-mother dyads. Front Cell Infect Microbiol. 2021;11:727630. doi: 10.3389/fcimb.2021.727630 EDN: KMKTNS</mixed-citation></ref><ref id="B10"><label>10.</label><mixed-citation>Hunsrisakhun J, Naorungroj S, Tangkuptanon W, et al. Impact of oral health chatbot with and without toothbrushing training on childhood caries. Int Dent J. 2025;75(2):1348–1359. doi: 10.1016/j.identj.2024.09.028 EDN: IPFNNQ</mixed-citation></ref><ref id="B11"><label>11.</label><mixed-citation>Rokhshad R, Banakar M, Shobeiri P, Zhang P. Artificial intelligence in early childhood caries detection and prediction: a systematic review and meta-analysis. Pediatr Dent. 2024;46(6):385–394.</mixed-citation></ref><ref id="B12"><label>12.</label><mixed-citation>Priyanka A, Sreekumar R, Naveen SN. Application of artificial intelligence technologies for the detection of early childhood caries. Discov Artif Intell. 2025;5(1):137. doi: 10.1007/s44163-025-00391-w EDN: XTNAER</mixed-citation></ref><ref id="B13"><label>13.</label><mixed-citation>Dipalma G, Inchingolo AM, Casamassima L, et al. Effectiveness of dental restorative materials in the atraumatic treatment of carious primary teeth in pediatric dentistry: a systematic review. Children. 2025;12(4):511. doi: 10.3390/children12040511 EDN: DSCBHX</mixed-citation></ref><ref id="B14"><label>14.</label><mixed-citation>Oliveira RDC, Camargo LB, Novaes TF, et al. Survival rate of primary molar restorations is not influenced by hand mixed or encapsulated GIC: 24 months RCT. BMC Oral Health. 2021;21(1):371. doi: 10.1186/s12903-021-01710-0 EDN: ZGRHBK</mixed-citation></ref><ref id="B15"><label>15.</label><mixed-citation>Garbim JR, Saihara CS, Olegário IC, et al. 2-year survival and cost analysis of occlusoproximal ART restorations using encapsulated glass ionomer cement in primary molars: a randomized controlled trial. BMC Oral Health. 2024;24(1):647. doi: 10.1186/s12903-024-04357-9 EDN: ICWOAQ</mixed-citation></ref><ref id="B16"><label>16.</label><mixed-citation>Pesaressi E, Zelada-Lopez D, Cosme T, et al. Randomised clinical trial of Class II ART restoration in primary teeth with and without retentive grooves after 12 months. Eur J Paediatr Dent. 2023;25(1):42–49. doi: 10.23804/ejpd.2023.1968</mixed-citation></ref><ref id="B17"><label>17.</label><mixed-citation>Patel MC, Makwani DA, Bhatt RK, et al. Evaluation of silver-modified atraumatic restorative technique versus conventional pulp therapy in asymptomatic deep carious lesion of primary molars – A comparative prospective clinical study. J Indian Soc Pedod Prev Dent. 2022;40(4):383–390. doi: 10.4103/jisppd.jisppd_360_22 EDN: IUIJZE</mixed-citation></ref><ref id="B18"><label>18.</label><mixed-citation>Özgür B, Kargın ST, Ölmez MS. Clinical evaluation of giomer- and resin-based fissure sealants on permanent molars affected by molar-incisor hypomineralization: a randomized clinical trial. BMC Oral Health. 2022;22(1):275. doi: 10.1186/s12903-022-02298-9 EDN: QHDINB</mixed-citation></ref><ref id="B19"><label>19.</label><mixed-citation>Pássaro A, Olegário I, Laux C, et al. Giomer composite compared to glass ionomer in occlusoproximal ART restorations of primary molars: 24-month RCT. Australian Dental Journal. 2022;67(2):148–158. doi: 10.1111/adj.12894 EDN: PWAXDX</mixed-citation></ref><ref id="B20"><label>20.</label><mixed-citation>Mahfouz RA, Hanno AG, Rahman AMAE. Zirconia-reinforced glass ionomer restorations in molar incisor hypomineralization: a randomized controlled clinical trial. Clin Oral Invest. 2025;29(6):290. doi: 10.1007/s00784-025-06352-y EDN: GVGVUO</mixed-citation></ref><ref id="B21"><label>21.</label><mixed-citation>Satyarup D, Mohanty S, Nagarajappa R, et al. Comparison of the effectiveness of 38% silver diamine fluoride and atraumatic restorative treatment for treating dental caries in a school setting: A randomized clinical trial. Dent Med Probl. 2022;59(2):217–223. doi: 10.17219/dmp/143547 EDN: OBMHDX</mixed-citation></ref><ref id="B22"><label>22.</label><mixed-citation>Gerihan HE, Çoğulu D, Önçağ Ö, et al. Assessment of MMP levels in reversible and irreversible pulpitis and a randomized controlled trial comparing clinical success of two different calcium-silicate cements in pulpotomy treatment of primary molars with an 18-month follow-up. BMC Oral Health. 2024;24(1):1020. doi: 10.1186/s12903-024-04795-5 EDN: KKIDHM</mixed-citation></ref><ref id="B23"><label>23.</label><mixed-citation>Gisour EF, Jahanimoghadam F, Karimipour P. Clinical and radiographic comparison of primary molar pulpotomy using formocresol, portland cement, and NeoMTA plus: a randomized controlled clinical trial. Sci Rep. 2024;14(1):29690. doi: 10.1038/s41598-024-81180-w EDN: YVOBCC</mixed-citation></ref><ref id="B24"><label>24.</label><mixed-citation>Khadilkar AS, Kapur A, Goyal A, et al. Comparison of clinical performance of obturating materials in pulpectomies: A randomized clinical trial. J Indian Soc Pedod Prev Dent. 2024;42(1):28–36. doi: 10.4103/jisppd.jisppd_516_23 EDN: UFNSFG</mixed-citation></ref><ref id="B25"><label>25.</label><mixed-citation>Narbutaite J, Santamaría RM, Innes N, et al. Comparison of three management approaches for dental caries in primary molars: A two-year randomized clinical trial. J Dent. 2024;150:105390. doi: 10.1016/j.jdent.2024.105390 EDN: OIWJYC</mixed-citation></ref><ref id="B26"><label>26.</label><mixed-citation>He Y, Vasilev K, Zilm P. pH-Responsive biomaterials for the treatment of dental caries—a focussed and critical review. Pharmaceutics. 2023;15(7):1837. doi: 10.3390/pharmaceutics15071837 EDN: OAGYDO</mixed-citation></ref><ref id="B27"><label>27.</label><mixed-citation>Schwarzmaier J, Frenkel E, Neumayr J, et al. Validation of an artificial intelligence-based model for early childhood caries detection in dental photographs. JCM. 2024;13(17):5215. doi: 10.3390/jcm13175215</mixed-citation></ref><ref id="B28"><label>28.</label><mixed-citation>Bennett L, Tiplady L, Taylor G. Can AI find the cavities in caries prediction and diagnosis? Evid Based Dent. 2025;26(3):139–140. doi: 10.1038/s41432-025-01181-0 EDN: RZKHOJ</mixed-citation></ref><ref id="B29"><label>29.</label><mixed-citation>Dezanetti JMP, Nascimento BL, Orsi JSR, Souza EM. Effectiveness of glass ionomer cements in the restorative treatment of radiation-related caries—a systematic review. Support Care Cancer. 2022;30(11):8667-8678. doi: 10.1007/s00520-022-07168-2 EDN: JEMTFI</mixed-citation></ref><ref id="B30"><label>30.</label><mixed-citation>Zhen L, Liang K, Luo J, et al. Mussel-inspired hydrogels for fluoride delivery and caries prevention. J Dent Res. 2022;101(13):1597–1605. doi: 10.1177/00220345221114783 EDN: CNMVCQ</mixed-citation></ref><ref id="B31"><label>31.</label><mixed-citation>Splieth CH, Banerjee A, Bottenberg P, et al. How to intervene in the caries process in children: a joint ORCA and EFCD expert delphi consensus statement. Caries Res. 2020;54(4):297–305. doi: 10.1159/000507692 EDN: KGEETK</mixed-citation></ref><ref id="B32"><label>32.</label><mixed-citation>Duangthip D, Fung MHT, Wong MCM, et al. Adverse effects of silver diamine fluoride treatment among preschool children. J Dent Res. 2018;97(4):395–401. doi: 10.1177/0022034517746678</mixed-citation></ref><ref id="B33"><label>33.</label><mixed-citation>Ganatra HA. Machine learning in pediatric healthcare: current trends, challenges, and future directions. JCM. 2025;14(3):807. doi: 10.3390/jcm14030807</mixed-citation></ref></ref-list></back></article>
