{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T23:54:20Z","timestamp":1781740460696,"version":"3.54.5"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T00:00:00Z","timestamp":1776470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T00:00:00Z","timestamp":1781740800000},"content-version":"vor","delay-in-days":61,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFC2705801"],"award-info":[{"award-number":["2023YFC2705801"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100022963","name":"Key Research and Development Program of Zhejiang Province","doi-asserted-by":"publisher","award":["2025C02115"],"award-info":[{"award-number":["2025C02115"]}],"id":[{"id":"10.13039\/100022963","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82071665"],"award-info":[{"award-number":["82071665"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-026-02628-z","type":"journal-article","created":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T15:47:00Z","timestamp":1776527220000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Patch-to-slide fusion deep learning model for histological diagnosis of early pregnancy loss including hydatidiform mole"],"prefix":"10.1038","volume":"9","author":[{"given":"Yating","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinhui","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqi","family":"Ning","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Limeng","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peilin","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huifang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangang","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huafeng","family":"Shou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingmei","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiqi","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qihong","family":"Wan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiupu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiwei","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanting","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haixia","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jue","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianhua","family":"Qian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,18]]},"reference":[{"key":"2628_CR1","doi-asserted-by":"publisher","first-page":"1400","DOI":"10.1056\/NEJMra000763","volume":"345","author":"ER Norwitz","year":"2001","unstructured":"Norwitz, E. R., Schust, D. J. & Fisher, S. J. Implantation and the survival of early pregnancy. N. Engl. J. Med. 345, 1400\u20131408 (2001).","journal-title":"N. Engl. J. Med."},{"key":"2628_CR2","unstructured":"Hinshaw, K. & Fayyad, A. The Management of Early Pregnancy Loss (Royal College of Obstetricians and Gynaecologists, 2000)."},{"key":"2628_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2021.e06359","volume":"7","author":"RMA Novais Nogueira Cardoso","year":"2021","unstructured":"Novais Nogueira Cardoso, R. M. A. et al. First-trimester miscarriage: a histopathological classification proposal. Heliyon 7, e06359 (2021).","journal-title":"Heliyon"},{"key":"2628_CR4","doi-asserted-by":"publisher","first-page":"942","DOI":"10.1097\/01.pas.0000157996.23059.c1","volume":"29","author":"M Fukunaga","year":"2005","unstructured":"Fukunaga, M. et al. Interobserver and intraobserver variability in the diagnosis of hydatidiform mole. Am. J. Surg. Pathol. 29, 942\u2013947 (2005).","journal-title":"Am. J. Surg. Pathol."},{"key":"2628_CR5","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.placenta.2004.05.011","volume":"26","author":"E Jauniaux","year":"2005","unstructured":"Jauniaux, E. & Burton, G. J. Pathophysiology of histological changes in early pregnancy loss. Placenta 26, 114\u2013123 (2005).","journal-title":"Placenta"},{"key":"2628_CR6","doi-asserted-by":"publisher","first-page":"525","DOI":"10.2353\/jmoldx.2010.090184","volume":"12","author":"A Norris-Kirby","year":"2010","unstructured":"Norris-Kirby, A. et al. Abnormal villous morphology associated with triple trisomy of paternal origin. J. Mol. Diagn. 12, 525\u2013529 (2010).","journal-title":"J. Mol. Diagn."},{"key":"2628_CR7","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1016\/j.fertnstert.2019.06.015","volume":"112","author":"MK Maisenbacher","year":"2019","unstructured":"Maisenbacher, M. K., Merrion, K. & Kutteh, W. H. Single-nucleotide polymorphism microarray detects molar pregnancies in 3% of miscarriages. Fertil. Steril. 112, 700\u2013706 (2019).","journal-title":"Fertil. Steril."},{"key":"2628_CR8","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1097\/IGC.0000000000000754","volume":"26","author":"TJ Colgan","year":"2016","unstructured":"Colgan, T. J., Chang, M. C., Nanji, S. & Kolomietz, E. A reappraisal of the incidence of placental hydatidiform mole using selective molecular genotyping. Int. J. Gynecol. Cancer 26, 1345\u20131350 (2016).","journal-title":"Int. J. Gynecol. Cancer"},{"key":"2628_CR9","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1080\/14737159.2022.2118050","volume":"22","author":"Y Zhao","year":"2022","unstructured":"Zhao, Y. et al. Challenges in diagnosing hydatidiform moles: a review of promising molecular biomarkers. Expert Rev. Mol. Diagn. 22, 783\u2013796 (2022).","journal-title":"Expert Rev. Mol. Diagn."},{"key":"2628_CR10","doi-asserted-by":"publisher","first-page":"1485","DOI":"10.5858\/arpa.2018-0226-RA","volume":"142","author":"BM Ronnett","year":"2018","unstructured":"Ronnett, B. M. Hydatidiform moles: ancillary techniques to refine diagnosis. Arch. Pathol. Lab Med. 142, 1485\u20131502 (2018).","journal-title":"Arch. Pathol. Lab Med."},{"key":"2628_CR11","doi-asserted-by":"publisher","first-page":"e265","DOI":"10.1016\/S2589-7500(23)00027-4","volume":"5","author":"Y Tolkach","year":"2023","unstructured":"Tolkach, Y. et al. Artificial intelligence for tumour tissue detection and histological regression grading in oesophageal adenocarcinomas: a retrospective algorithm development and validation study. Lancet Digit Health 5, e265\u2013e275 (2023).","journal-title":"Lancet Digit Health"},{"key":"2628_CR12","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.semcancer.2023.09.001","volume":"96","author":"J Zhang","year":"2023","unstructured":"Zhang, J. et al. Recent advancements in artificial intelligence for breast cancer: image augmentation, segmentation, diagnosis, and prognosis approaches. Semin Cancer Biol. 96, 11\u201325 (2023).","journal-title":"Semin Cancer Biol."},{"key":"2628_CR13","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.adr1576","volume":"11","author":"N Mao","year":"2025","unstructured":"Mao, N. et al. A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer. Sci. Adv. 11, eadr1576 (2025).","journal-title":"Sci. Adv."},{"key":"2628_CR14","doi-asserted-by":"publisher","first-page":"1595","DOI":"10.1109\/JBHI.2015.2483318","volume":"20","author":"P Guo","year":"2016","unstructured":"Guo, P. et al. Nuclei-based features for uterine cervical cancer histology image analysis with fusion-based classification. IEEE J. Biomed. Health Inf. 20, 1595\u20131607 (2016).","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"2628_CR15","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1002\/1096-9896(2000)9999:9999<::AID-PATH708>3.0.CO;2-I","volume":"192","author":"SJ Keenan","year":"2000","unstructured":"Keenan, S. J. et al. An automated machine vision system for the histological grading of cervical intraepithelial neoplasia (CIN). J. Pathol. 192, 351\u2013362 (2000).","journal-title":"J. Pathol."},{"key":"2628_CR16","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-18147-8","volume":"11","author":"Z Song","year":"2020","unstructured":"Song, Z. et al. Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning. Nat. Commun. 11, 4294 (2020).","journal-title":"Nat. Commun."},{"key":"2628_CR17","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/S1470-2045(20)30535-0","volume":"22","author":"R Yamashita","year":"2021","unstructured":"Yamashita, R. et al. Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study. Lancet Oncol. 22, 132\u2013141 (2021).","journal-title":"Lancet Oncol."},{"key":"2628_CR18","doi-asserted-by":"publisher","DOI":"10.1186\/s13073-021-00968-x","volume":"13","author":"KA Tran","year":"2021","unstructured":"Tran, K. A. et al. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med. 13, 152 (2021).","journal-title":"Genome Med."},{"key":"2628_CR19","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.canlet.2019.12.007","volume":"471","author":"S Huang","year":"2020","unstructured":"Huang, S., Yang, J., Fong, S. & Zhao, Q. Artificial intelligence in cancer diagnosis and prognosis: opportunities and challenges. Cancer Lett. 471, 61\u201371 (2020).","journal-title":"Cancer Lett."},{"key":"2628_CR20","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1109\/TMI.2022.3206605","volume":"42","author":"X Wei","year":"2023","unstructured":"Wei, X. et al. 3D soma detection in large-scale whole brain images via a two-stage neural network. IEEE Trans. Med Imaging 42, 148\u2013157 (2023).","journal-title":"IEEE Trans. Med Imaging"},{"key":"2628_CR21","unstructured":"Nazeri, K., Aminpour, A. & Ebrahimi, M. In Image Analysis and Recognition (eds Campilho, A., Karray, F. & ter Haar Romeny, B.) (Springer International Publishing, 2018)."},{"key":"2628_CR22","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1038\/s41591-019-0508-1","volume":"25","author":"G Campanella","year":"2019","unstructured":"Campanella, G. et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med 25, 1301\u20131309 (2019).","journal-title":"Nat. Med"},{"key":"2628_CR23","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1016\/j.ajpath.2023.02.008","volume":"193","author":"Z Shi","year":"2023","unstructured":"Shi, Z. et al. A two-stage end-to-end deep learning framework for pathologic examination in skin tumor diagnosis. Am. J. Pathol. 193, 769\u2013777 (2023).","journal-title":"Am. J. Pathol."},{"key":"2628_CR24","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1038\/s41551-020-00682-w","volume":"5","author":"MY Lu","year":"2021","unstructured":"Lu, M. Y. et al. Data-efficient and weakly supervised computational pathology on whole-slide images. Nat. Biomed. Eng. 5, 555\u2013570 (2021).","journal-title":"Nat. Biomed. Eng."},{"key":"2628_CR25","unstructured":"Shao, Z. et al. TransMIL: transformer based correlated multiple instance learning for whole slide image classification. In Neural Information Processing Systems (2021)."},{"key":"2628_CR26","doi-asserted-by":"publisher","first-page":"044501","DOI":"10.1117\/1.JMI.6.4.044501","volume":"6","author":"P Palee","year":"2019","unstructured":"Palee, P. et al. Heuristic neural network approach in histological sections detection of hydatidiform mole. J. Med. Imaging 6, 044501 (2019).","journal-title":"J. Med. Imaging"},{"key":"2628_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107510","volume":"234","author":"C Zhu","year":"2023","unstructured":"Zhu, C. et al. A real-time computer-aided diagnosis method for hydatidiform mole recognition using deep neural network. Comput. Methods Prog. Biomed. 234, 107510 (2023).","journal-title":"Comput. Methods Prog. Biomed."},{"key":"2628_CR28","doi-asserted-by":"publisher","first-page":"1396","DOI":"10.6004\/jnccn.2019.7364","volume":"17","author":"KM Elias","year":"2019","unstructured":"Elias, K. M., Berkowitz, R. S. & Horowitz, N. S. State-of-the-art workup and initial management of newly diagnosed molar pregnancy and postmolar gestational trophoblastic neoplasia. J. Natl. Compr. Cancer Netw. 17, 1396\u20131401 (2019).","journal-title":"J. Natl. Compr. Cancer Netw."},{"key":"2628_CR29","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1016\/S0140-6736(10)60280-2","volume":"376","author":"MJ Seckl","year":"2010","unstructured":"Seckl, M. J., Sebire, N. J. & Berkowitz, R. S. Gestational trophoblastic disease. Lancet 376, 717\u2013729 (2010).","journal-title":"Lancet"},{"key":"2628_CR30","doi-asserted-by":"crossref","unstructured":"Zhao, Y. et al. Reappraisal and refined diagnosis of ultrasonography and histological findings for hydatidiform moles: a multicentre retrospective study of 821 patients. J. Clin. Pathol. 78, 483\u2013494 (2025).","DOI":"10.1136\/jcp-2024-209638"},{"key":"2628_CR31","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1111\/his.15247","volume":"85","author":"A Nagy","year":"2024","unstructured":"Nagy, A. et al. Diandric triploid partial mole versus digynic nonmolar triploidy: is morphological assessment sufficient for the diagnostic distinction? Histopathology 85, 879\u2013888 (2024).","journal-title":"Histopathology"},{"key":"2628_CR32","first-page":"1105","volume":"38","author":"Y Zhao","year":"2018","unstructured":"Zhao, Y., Xiong, G. W., Zhang, X. W. & Hang, B. O. Is Ki-67 of diagnostic value in distinguishing between partial and complete hydatidiform moles? A systematic review and meta-analysis. Anticancer Res 38, 1105\u20131110 (2018).","journal-title":"Anticancer Res"},{"key":"2628_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/ijms27010142","volume":"27","author":"TA Balan","year":"2025","unstructured":"Balan, T. A. et al. Hydatidiform moles: the contribution of ancillary techniques in refining their histopathological diagnosis. Int. J. Mol. Sci. 27, 1\u201318 (2025).","journal-title":"Int. J. Mol. Sci."},{"key":"2628_CR34","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/0197-0070(88)90075-7","volume":"9","author":"MC Savedra","year":"1988","unstructured":"Savedra, M. C. & Highley, B. L. Photography. Is it useful in learning how adolescents view hospitalization? J. Adolesc. Health Care 9, 219\u2013224 (1988).","journal-title":"J. Adolesc. Health Care"},{"key":"2628_CR35","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1002\/path.6057","volume":"260","author":"NC Blessin","year":"2023","unstructured":"Blessin, N. C. et al. Automated Ki-67 labeling index assessment in prostate cancer using artificial intelligence and multiplex fluorescence immunohistochemistry. J. Pathol. 260, 5\u201316 (2023).","journal-title":"J. Pathol."},{"key":"2628_CR36","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1021\/acs.analchem.1c04000","volume":"94","author":"L Fan","year":"2022","unstructured":"Fan, L. et al. Artificial intelligence-aided multiple tumor detection method based on immunohistochemistry-enhanced dark-field imaging. Anal. Chem. 94, 1037\u20131045 (2022).","journal-title":"Anal. Chem."},{"key":"2628_CR37","doi-asserted-by":"publisher","first-page":"970","DOI":"10.1038\/s41586-024-07894-z","volume":"634","author":"X Wang","year":"2024","unstructured":"Wang, X. et al. A pathology foundation model for cancer diagnosis and prognosis prediction. Nature 634, 970\u2013978 (2024).","journal-title":"Nature"},{"key":"2628_CR38","doi-asserted-by":"publisher","DOI":"10.1038\/s41523-021-00357-y","volume":"7","author":"Y Ektefaie","year":"2021","unstructured":"Ektefaie, Y. et al. Integrative multiomics-histopathology analysis for breast cancer classification. NPJ Breast Cancer 7, 147 (2021).","journal-title":"NPJ Breast Cancer"},{"key":"2628_CR39","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1146\/annurev-pathol-052016-100237","volume":"12","author":"P Hui","year":"2017","unstructured":"Hui, P., Buza, N., Murphy, K. M. & Ronnett, B. M. Hydatidiform moles: genetic basis and precision diagnosis. Annu. Rev. Pathol. 12, 449\u2013485 (2017).","journal-title":"Annu. Rev. Pathol."},{"key":"2628_CR40","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-17204-5","volume":"7","author":"P Bankhead","year":"2017","unstructured":"Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).","journal-title":"Sci. Rep."},{"key":"2628_CR41","doi-asserted-by":"crossref","unstructured":"Macenko, M. et al. A method for normalizing histology slides for quantitative analysis. In 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (2009).","DOI":"10.1109\/ISBI.2009.5193250"},{"key":"2628_CR42","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016).","DOI":"10.1109\/CVPR.2016.90"},{"key":"2628_CR43","unstructured":"Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. CoRR abs\/1412.6980, (2014)."},{"key":"2628_CR44","doi-asserted-by":"publisher","unstructured":"Shao, Z. et al. TransMIL: transformer based correlated multiple instance learning for whole slide image classification. https:\/\/doi.org\/10.48550\/arXiv.2106.00908 (2021).","DOI":"10.48550\/arXiv.2106.00908"}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02628-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02628-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02628-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T23:27:25Z","timestamp":1781738845000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02628-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,18]]},"references-count":44,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2628"],"URL":"https:\/\/doi.org\/10.1038\/s41746-026-02628-z","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,18]]},"assertion":[{"value":"28 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"472"}}