{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T12:48:43Z","timestamp":1774270123001,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T00:00:00Z","timestamp":1755648000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T00:00:00Z","timestamp":1755648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Shenzhen Science and Technology Program","award":["JCYJ20240813144105007"],"award-info":[{"award-number":["JCYJ20240813144105007"]}]},{"name":"Shenzhen Science and Technology Program","award":["20240724152335001"],"award-info":[{"award-number":["20240724152335001"]}]},{"name":"Shenzhen Science and Technology Program","award":["20240724152335001"],"award-info":[{"award-number":["20240724152335001"]}]},{"name":"Shenzhen Science and Technology Program","award":["JCYJ20240813144105007"],"award-info":[{"award-number":["JCYJ20240813144105007"]}]},{"name":"Science and Technology Program of Longgang District, Shenzhen","award":["LGKCYLWS2023019"],"award-info":[{"award-number":["LGKCYLWS2023019"]}]},{"name":"Science and Technology Program of Longgang District, Shenzhen","award":["LGKCYLWS2023019"],"award-info":[{"award-number":["LGKCYLWS2023019"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82088102"],"award-info":[{"award-number":["82088102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"CAMS Innovation Fund for Medical Sciences","award":["2019-I2M-5-064"],"award-info":[{"award-number":["2019-I2M-5-064"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-025-01942-2","type":"journal-article","created":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T16:20:19Z","timestamp":1755706819000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A novel sequence-based transformer model architecture for integrating multi-omics data in preterm birth risk prediction"],"prefix":"10.1038","volume":"8","author":[{"given":"Si","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Chenchen","family":"Guan","sequence":"additional","affiliation":[]},{"given":"Siwei","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Yibing","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Wenzhi","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Xiao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xinrui","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jinying","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Shida","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Jianguo","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yongcheng","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Danling","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Hai-Xi","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Lijian","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Hefeng","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,20]]},"reference":[{"key":"1942_CR1","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1001\/jama.2023.7244","volume":"330","author":"DJ Dudley","year":"2023","unstructured":"Dudley, D. J. & Ennen, C. S. The vexing problem of preterm birth prevention. Jama 330, 323\u2013325 (2023).","journal-title":"Jama"},{"key":"1942_CR2","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1001\/jama.2024.20957","volume":"333","author":"E Tsamantioti","year":"2025","unstructured":"Tsamantioti, E., Sandstr\u00f6m, A., Lindblad Wollmann, C., Snowden, J. M. & Razaz, N. Association of severe maternal morbidity with subsequent birth. Jama 333, 133\u2013142 (2025).","journal-title":"Jama"},{"key":"1942_CR3","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1056\/NEJMra2303347","volume":"389","author":"TE Inder","year":"2023","unstructured":"Inder, T. E., Volpe, J. J. & Anderson, P. J. Defining the neurologic consequences of preterm birth. N. Engl. J. Med. 389, 441\u2013453 (2023).","journal-title":"N. Engl. J. Med."},{"key":"1942_CR4","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1097\/AOG.0000000000004612","volume":"138","author":"MK Hoffman","year":"2021","unstructured":"Hoffman, M. K. Prediction and prevention of spontaneous preterm birth: ACOG practice bulletin, number 234. Obstet. Gynecol. 138, 945\u2013946 (2021).","journal-title":"Obstet. Gynecol."},{"key":"1942_CR5","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1038\/s41564-022-01293-8","volume":"8","author":"WF Kindschuh","year":"2023","unstructured":"Kindschuh, W. F. et al. Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome. Nat. Microbiol. 8, 246\u2013259 (2023).","journal-title":"Nat. Microbiol."},{"key":"1942_CR6","doi-asserted-by":"publisher","first-page":"2886","DOI":"10.1038\/s41591-024-03139-8","volume":"30","author":"J Li","year":"2024","unstructured":"Li, J. et al. Integrated image-based deep learning and language models for primary diabetes care. Nat. Med. 30, 2886\u20132896 (2024).","journal-title":"Nat. Med."},{"key":"1942_CR7","volume":"48","author":"M Dalakoti","year":"2024","unstructured":"Dalakoti, M. et al. Incorporating AI into cardiovascular diseases prevention-insights from Singapore. Lancet Reg. Health West Pac. 48, 101102 (2024).","journal-title":"Lancet Reg. Health West Pac."},{"key":"1942_CR8","unstructured":"Deng, S. et al. GeneLLM: a large cfRNA language model for cancer screening from raw reads. bioRxiv https:\/\/www.biorxiv.org\/content\/10.1101\/2024.06.29.601341v1 (2024)."},{"key":"1942_CR9","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1038\/s41591-023-02774-x","volume":"30","author":"J Zhang","year":"2024","unstructured":"Zhang, J. et al. Prospective prenatal cell-free DNA screening for genetic conditions of heterogenous etiologies. Nat. Med. 30, 470\u2013479 (2024).","journal-title":"Nat. Med."},{"key":"1942_CR10","doi-asserted-by":"publisher","first-page":"762","DOI":"10.1016\/S0140-6736(22)02610-1","volume":"401","author":"T Schlaikj\u00e6r Hartwig","year":"2023","unstructured":"Schlaikj\u00e6r Hartwig, T. et al. Cell-free fetal DNA for genetic evaluation in Copenhagen Pregnancy Loss Study (COPL): a prospective cohort study. Lancet 401, 762\u2013771 (2023).","journal-title":"Lancet"},{"key":"1942_CR11","doi-asserted-by":"publisher","first-page":"553.e551","DOI":"10.1016\/j.ajog.2023.05.015","volume":"229","author":"S Zhou","year":"2023","unstructured":"Zhou, S. et al. Noninvasive preeclampsia prediction using plasma cell-free RNA signatures. Am. J. Obstet. Gynecol. 229, 553.e551\u2013553.e516 (2023).","journal-title":"Am. J. Obstet. Gynecol."},{"key":"1942_CR12","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1038\/s41586-022-04410-z","volume":"602","author":"MN Moufarrej","year":"2022","unstructured":"Moufarrej, M. N. et al. Early prediction of preeclampsia in pregnancy with cell-free RNA. Nature 602, 689\u2013694 (2022).","journal-title":"Nature"},{"key":"1942_CR13","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1016\/j.ajog.2023.03.009","volume":"228","author":"A Gr\u00fcnebaum","year":"2023","unstructured":"Gr\u00fcnebaum, A., Chervenak, J., Pollet, S. L., Katz, A. & Chervenak, F. A. The exciting potential for ChatGPT in obstetrics and gynecology. Am. J. Obstet. Gynecol. 228, 696\u2013705 (2023).","journal-title":"Am. J. Obstet. Gynecol."},{"key":"1942_CR14","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-024-03397-2","volume":"25","author":"ZC Fu","year":"2024","unstructured":"Fu, Z. C., Gao, B. Q., Nan, F., Ma, X. K. & Yang, L. DEMINING: a deep learning model embedded framework to distinguish RNA editing from DNA mutations in RNA sequencing data. Genome Biol. 25, 258 (2024).","journal-title":"Genome Biol."},{"key":"1942_CR15","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1038\/s41423-020-0471-2","volume":"17","author":"D Miller","year":"2020","unstructured":"Miller, D., Gershater, M., Slutsky, R., Romero, R. & Gomez-Lopez, N. Maternal and fetal T cells in term pregnancy and preterm labor. Cell Mol. Immunol. 17, 693\u2013704 (2020).","journal-title":"Cell Mol. Immunol."},{"key":"1942_CR16","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1038\/nature12986","volume":"505","author":"Y Tay","year":"2014","unstructured":"Tay, Y., Rinn, J. & Pandolfi, P. P. The multilayered complexity of ceRNA crosstalk and competition. Nature 505, 344\u2013352 (2014).","journal-title":"Nature"},{"key":"1942_CR17","doi-asserted-by":"publisher","DOI":"10.1038\/s41419-021-04165-x","volume":"12","author":"C Burgaletto","year":"2021","unstructured":"Burgaletto, C. et al. Targeting the miRNA-155\/TNFSF10 network restrains inflammatory response in the retina in a mouse model of Alzheimer\u2019s disease. Cell Death Dis. 12, 905 (2021).","journal-title":"Cell Death Dis."},{"key":"1942_CR18","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1016\/j.cell.2013.03.043","volume":"153","author":"A Helwak","year":"2013","unstructured":"Helwak, A., Kudla, G., Dudnakova, T. & Tollervey, D. Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell 153, 654\u2013665 (2013).","journal-title":"Cell"},{"key":"1942_CR19","doi-asserted-by":"publisher","first-page":"21","DOI":"10.4049\/jimmunol.0902369","volume":"184","author":"Y Su\u00e1rez","year":"2010","unstructured":"Su\u00e1rez, Y., Wang, C., Manes, T. D. & Pober, J. S. Cutting edge: TNF-induced microRNAs regulate TNF-induced expression of E-selectin and intercellular adhesion molecule-1 on human endothelial cells: feedback control of inflammation. J. Immunol. 184, 21\u201325 (2010).","journal-title":"J. Immunol."},{"key":"1942_CR20","doi-asserted-by":"publisher","first-page":"eadd2029","DOI":"10.1126\/scitranslmed.add2029","volume":"16","author":"YY Tai","year":"2024","unstructured":"Tai, Y. Y. et al. Allele-specific control of rodent and human lncRNA KMT2E-AS1 promotes hypoxic endothelial pathology in pulmonary hypertension. Sci. Transl. Med. 16, eadd2029 (2024).","journal-title":"Sci. Transl. Med."},{"key":"1942_CR21","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1186\/s11658-021-00264-x","volume":"26","author":"J Ni","year":"2021","unstructured":"Ni, J., Huang, Z. & Wang, D. LncRNA TP73-AS1 promotes oxidized low-density lipoprotein-induced apoptosis of endothelial cells in atherosclerosis by targeting the miR-654-3p\/AKT3 axis. Cell Mol. Biol. Lett. 26, 27 (2021).","journal-title":"Cell Mol. Biol. Lett."},{"key":"1942_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.xcrm.2021.100323","volume":"2","author":"AL Tarca","year":"2021","unstructured":"Tarca, A. L. et al. Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth. Cell Rep. Med. 2, 100323 (2021).","journal-title":"Cell Rep. Med."},{"key":"1942_CR23","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1056\/NEJMra1705345","volume":"379","author":"DW Bianchi","year":"2018","unstructured":"Bianchi, D. W. & Chiu, R. W. K. Sequencing of circulating cell-free DNA during pregnancy. N. Engl. J. Med. 379, 464\u2013473 (2018).","journal-title":"N. Engl. J. Med."},{"key":"1942_CR24","doi-asserted-by":"crossref","unstructured":"Aghaeepour, N. et al. An immune clock of human pregnancy. Science https:\/\/www.science.org\/doi\/10.1126\/sciimmunol.aan2946 (2017).","DOI":"10.1126\/sciimmunol.aan2946"},{"key":"1942_CR25","doi-asserted-by":"crossref","unstructured":"Enninga, E. A. L. et al. Immune checkpoint molecules soluble program death ligand 1 and galectin-9 are increased in pregnancy. Am. J. Reprod. Immunol. https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/aji.12795 (2018).","DOI":"10.1111\/aji.12795"},{"key":"1942_CR26","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1007\/s00281-018-0680-2","volume":"40","author":"S Nair","year":"2018","unstructured":"Nair, S. & Salomon, C. Extracellular vesicles and their immunomodulatory functions in pregnancy. Semin Immunopathol. 40, 425\u2013437 (2018).","journal-title":"Semin Immunopathol."},{"key":"1942_CR27","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.1007\/s10654-024-01157-x","volume":"39","author":"S Zhou","year":"2024","unstructured":"Zhou, S. et al. A prospective multicenter birth cohort in China: pregnancy health atlas. Eur. J. Epidemiol. 39, 1297\u20131310 (2024).","journal-title":"Eur. J. Epidemiol."},{"key":"1942_CR28","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1016\/j.cell.2018.08.016","volume":"175","author":"S Liu","year":"2018","unstructured":"Liu, S. et al. Genomic analyses from non-invasive prenatal testing reveal genetic associations, patterns of viral infections, and Chinese population history. Cell 175, 347\u2013359.e314 (2018).","journal-title":"Cell"},{"key":"1942_CR29","doi-asserted-by":"publisher","DOI":"10.1002\/ctm2.987","volume":"12","author":"Z Liu","year":"2022","unstructured":"Liu, Z. et al. Polyadenylation ligation-mediated sequencing (PALM-Seq) characterizes cell-free coding and non-coding RNAs in human biofluids. Clin. Transl. Med. 12, e987 (2022).","journal-title":"Clin. Transl. Med."},{"key":"1942_CR30","doi-asserted-by":"publisher","first-page":"1277","DOI":"10.1038\/s41591-022-01850-y","volume":"28","author":"L Niu","year":"2022","unstructured":"Niu, L. et al. Noninvasive proteomic biomarkers for alcohol-related liver disease. Nat. Med. 28, 1277\u20131287 (2022).","journal-title":"Nat. Med."},{"key":"1942_CR31","doi-asserted-by":"publisher","first-page":"i884","DOI":"10.1093\/bioinformatics\/bty560","volume":"34","author":"S Chen","year":"2018","unstructured":"Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884\u2013i890 (2018).","journal-title":"Bioinformatics"},{"key":"1942_CR32","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/nmeth.3317","volume":"12","author":"D Kim","year":"2015","unstructured":"Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357\u2013360 (2015).","journal-title":"Nat. Methods"},{"key":"1942_CR33","doi-asserted-by":"publisher","first-page":"1650","DOI":"10.1038\/nprot.2016.095","volume":"11","author":"M Pertea","year":"2016","unstructured":"Pertea, M., Kim, D., Pertea, G. M., Leek, J. T. & Salzberg, S. L. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat. Protoc. 11, 1650\u20131667 (2016).","journal-title":"Nat. Protoc."},{"key":"1942_CR34","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-014-0550-8","volume":"15","author":"MI Love","year":"2014","unstructured":"Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).","journal-title":"Genome Biol."},{"key":"1942_CR35","volume":"2","author":"T Wu","year":"2021","unstructured":"Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).","journal-title":"Innovation"},{"key":"1942_CR36","doi-asserted-by":"publisher","first-page":"107167","DOI":"10.18632\/oncotarget.22363","volume":"8","author":"T Kehl","year":"2017","unstructured":"Kehl, T. et al. About miRNAs, miRNA seeds, target genes and target pathways. Oncotarget 8, 107167\u2013107175 (2017).","journal-title":"Oncotarget"},{"key":"1942_CR37","doi-asserted-by":"crossref","unstructured":"Pertea, G. & Pertea, M. GFF Utilities: GffRead and GffCompare. F1000Res 9, ISCB Comm J-304 (2020).","DOI":"10.12688\/f1000research.23297.2"},{"key":"1942_CR38","doi-asserted-by":"publisher","first-page":"D155","DOI":"10.1093\/nar\/gky1141","volume":"47","author":"A Kozomara","year":"2019","unstructured":"Kozomara, A., Birgaoanu, M. & Griffiths-Jones, S. miRBase: from microRNA sequences to function. Nucleic Acids Res. 47, D155\u2013d162 (2019).","journal-title":"Nucleic Acids Res."},{"key":"1942_CR39","doi-asserted-by":"publisher","first-page":"D304","DOI":"10.1093\/nar\/gkad1071","volume":"52","author":"G Skoufos","year":"2024","unstructured":"Skoufos, G. et al. TarBase-v9.0 extends experimentally supported miRNA-gene interactions to cell-types and virally encoded miRNAs. Nucleic Acids Res. 52, D304\u2013d310 (2024).","journal-title":"Nucleic Acids Res."},{"key":"1942_CR40","doi-asserted-by":"publisher","first-page":"D222","DOI":"10.1093\/nar\/gkab1079","volume":"50","author":"HY Huang","year":"2022","unstructured":"Huang, H. Y. et al. miRTarBase update 2022: an informative resource for experimentally validated miRNA-target interactions. Nucleic Acids Res. 50, D222\u2013d230 (2022).","journal-title":"Nucleic Acids Res."},{"key":"1942_CR41","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-38232-y","volume":"14","author":"Y Dong","year":"2023","unstructured":"Dong, Y. et al. Identification of CircRNA signature associated with tumor immune infiltration to predict therapeutic efficacy of immunotherapy. Nat. Commun. 14, 2540 (2023).","journal-title":"Nat. Commun."},{"key":"1942_CR42","unstructured":"Chen, S. et al. Cancer type classification using plasma cell-free RNAs derived from human and microbes. Elife https:\/\/elifesciences.org\/articles\/75181 (2022)."},{"key":"1942_CR43","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1038\/s41551-021-00837-3","volume":"6","author":"P Song","year":"2022","unstructured":"Song, P. et al. Limitations and opportunities of technologies for the analysis of cell-free DNA in cancer diagnostics. Nat. Biomed. Eng. 6, 232\u2013245 (2022).","journal-title":"Nat. Biomed. Eng."},{"key":"1942_CR44","doi-asserted-by":"publisher","DOI":"10.1186\/s40246-024-00666-w","volume":"18","author":"T Wang","year":"2024","unstructured":"Wang, T. et al. Fast and accurate DNASeq variant calling workflow composed of LUSH toolkit. Hum. Genom. 18, 114 (2024).","journal-title":"Hum. Genom."},{"key":"1942_CR45","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2021.684238","volume":"8","author":"BW Han","year":"2021","unstructured":"Han, B. W. et al. A deep-learning pipeline for TSS coverage imputation from shallow cell-free DNA sequencing. Front. Med. 8, 684238 (2021).","journal-title":"Front. Med."},{"key":"1942_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.xcrm.2024.101660","volume":"5","author":"Z Tang","year":"2024","unstructured":"Tang, Z. et al. Longitudinal integrative cell-free DNA analysis in gestational diabetes mellitus. Cell Rep. Med. 5, 101660 (2024).","journal-title":"Cell Rep. Med."},{"key":"1942_CR47","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1093\/bioinformatics\/btq033","volume":"26","author":"AR Quinlan","year":"2010","unstructured":"Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841\u2013842 (2010).","journal-title":"Bioinformatics"},{"key":"1942_CR48","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-33327-4","volume":"13","author":"NS Maurya","year":"2023","unstructured":"Maurya, N. S., Kushwah, S., Kushwaha, S., Chawade, A. & Mani, A. Prognostic model development for classification of colorectal adenocarcinoma by using machine learning model based on feature selection technique Boruta. Sci. Rep. 13, 6413 (2023).","journal-title":"Sci. Rep."},{"key":"1942_CR49","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-023-05300-5","volume":"24","author":"H Zhou","year":"2023","unstructured":"Zhou, H., Xin, Y. & Li, S. A diabetes prediction model based on Boruta feature selection and ensemble learning. BMC Bioinform. 24, 224 (2023).","journal-title":"BMC Bioinform."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01942-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01942-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01942-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T10:20:24Z","timestamp":1757413224000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01942-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,20]]},"references-count":49,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1942"],"URL":"https:\/\/doi.org\/10.1038\/s41746-025-01942-2","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,20]]},"assertion":[{"value":"6 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 August 2025","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":"536"}}