{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T17:16:49Z","timestamp":1770139009496,"version":"3.49.0"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T00:00:00Z","timestamp":1757980800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T00:00:00Z","timestamp":1757980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Key Research and Development Program of China","award":["No. 2022YFC3902100"],"award-info":[{"award-number":["No. 2022YFC3902100"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["No. DUT24YG201"],"award-info":[{"award-number":["No. DUT24YG201"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s11517-025-03444-5","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T09:34:39Z","timestamp":1758015279000},"page":"147-163","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring universal segmentation models for automatic quantification of cardiac functional parameters from zebrafish heartbeat videos"],"prefix":"10.1007","volume":"64","author":[{"given":"Yali","family":"Wang","sequence":"first","affiliation":[]},{"given":"Haochun","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Xingye","family":"Qiao","sequence":"additional","affiliation":[]},{"given":"Fengyu","family":"Cong","sequence":"additional","affiliation":[]},{"given":"Yanbin","family":"Zhao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1305-0010","authenticated-orcid":false,"given":"Hongming","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,16]]},"reference":[{"key":"3444_CR1","doi-asserted-by":"publisher","first-page":"1027401","DOI":"10.3389\/fddsv.2022.1027401","volume":"2","author":"CC Hong","year":"2022","unstructured":"Hong CC (2022) The grand challenge of discovering new cardiovascular drugs. Front Drug Discov 2:1027401","journal-title":"Front Drug Discov"},{"key":"3444_CR2","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.exger.2017.06.015","volume":"109","author":"C Heiss","year":"2018","unstructured":"Heiss C, Spyridopoulos I, Haendeler J (2018) Interventions to slow cardiovascular aging: dietary restriction, drugs and novel molecules. Exp Gerontol 109:108\u2013118","journal-title":"Exp Gerontol"},{"key":"3444_CR3","doi-asserted-by":"crossref","unstructured":"Rahman Khan F, Sulaiman Alhewairini S (2019) Zebrafish (Danio rerio) as a model organism. Curr Trends Cancer Manag 81517(10.5772)","DOI":"10.5772\/intechopen.81517"},{"issue":"5","key":"3444_CR4","doi-asserted-by":"publisher","first-page":"900","DOI":"10.1111\/bph.15473","volume":"179","author":"G Bowley","year":"2022","unstructured":"Bowley G, Kugler E, Wilkinson R, Lawrie A, Eeden F, Chico TJ, Evans PC, No\u00ebl ES, Serbanovic-Canic J (2022) Zebrafish as a tractable model of human cardiovascular disease. Br J Pharmacol 179(5):900\u2013917","journal-title":"Br J Pharmacol"},{"issue":"2","key":"3444_CR5","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1080\/19336950.2015.1121335","volume":"10","author":"M Vornanen","year":"2016","unstructured":"Vornanen M, Hassinen M (2016) Zebrafish heart as a model for human cardiac electrophysiology. Channels 10(2):101\u2013110","journal-title":"Channels"},{"key":"3444_CR6","doi-asserted-by":"publisher","first-page":"489","DOI":"10.3389\/fendo.2020.00489","volume":"11","author":"F Tonelli","year":"2020","unstructured":"Tonelli F, Bek JW, Besio R, De Clercq A, Leoni L, Salmon P, Coucke PJ, Willaert A, Forlino A (2020) Zebrafish: a resourceful vertebrate model to investigate skeletal disorders. Front Endocrinol 11:489","journal-title":"Front Endocrinol"},{"key":"3444_CR7","doi-asserted-by":"crossref","unstructured":"Naderi AM, Bu H, Su J, Huang M-H, Vo K, Torres RST, Chiao J-C, Lee J, Lau MP, Xu X et al (2021) Deep learning-based framework for cardiac function assessment in embryonic zebrafish from heart beating videos. Comput Biol Med 135:104565","DOI":"10.1016\/j.compbiomed.2021.104565"},{"issue":"11","key":"3444_CR8","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1002\/dvdy.24497","volume":"246","author":"HC Yalcin","year":"2017","unstructured":"Yalcin HC, Amindari A, Butcher JT, Althani A, Yacoub M (2017) Heart function and hemodynamic analysis for zebrafish embryos. Dev Dyn 246(11):868\u2013880","journal-title":"Dev Dyn"},{"issue":"6","key":"3444_CR9","doi-asserted-by":"publisher","first-page":"341","DOI":"10.3390\/info14060341","volume":"14","author":"M-H Huang","year":"2023","unstructured":"Huang M-H, Naderi AM, Zhu P, Xu X, Cao H (2023) Assessing cardiac functions of zebrafish from echocardiography using deep learning. Information 14(6):341","journal-title":"Information"},{"issue":"1","key":"3444_CR10","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1515\/cdbme-2016-0052","volume":"2","author":"S Nasrat","year":"2016","unstructured":"Nasrat S, Marcato D, Hirth S, Reischl M, Pylatiuk C (2016) Semi-automated detection of fractional shortening in zebrafish embryo heart videos. Curr Direct Biomed Eng 2(1):233\u2013236","journal-title":"Curr Direct Biomed Eng"},{"issue":"9","key":"3444_CR11","doi-asserted-by":"publisher","first-page":"329","DOI":"10.3390\/biomedicines8090329","volume":"8","author":"F Santoso","year":"2020","unstructured":"Santoso F, Farhan A, Castillo AL, Malhotra N, Saputra F, Kurnia KA, Chen KH-C, Huang J-C, Chen J-R, Hsiao C-D (2020) An overview of methods for cardiac rhythm detection in zebrafish. Biomedicines 8(9):329","journal-title":"Biomedicines"},{"key":"3444_CR12","doi-asserted-by":"crossref","unstructured":"Munaf\u00f2 R, Saitta S, Tondi D, Ingallina G, Denti P, Maisano F, Votta E et al (2025) Automatic 4d mitral valve segmentation from transesophageal echocardiography: a semi-supervised learning approach. Med Biol Eng Comput pp 1\u201316","DOI":"10.1007\/s11517-024-03275-w"},{"issue":"2","key":"3444_CR13","doi-asserted-by":"publisher","first-page":"101","DOI":"10.2144\/000113078","volume":"46","author":"M Fink","year":"2009","unstructured":"Fink M, Callol-Massot C, Chu A, Ruiz-Lozano P, Belmonte JCI, Giles W, Bodmer R, Ocorr K (2009) A new method for detection and quantification of heartbeat parameters in drosophila, zebrafish, and embryonic mouse hearts. Biotechniques 46(2):101\u2013113","journal-title":"Biotechniques"},{"issue":"4","key":"3444_CR14","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1089\/zeb.2014.1002","volume":"11","author":"C Pylatiuk","year":"2014","unstructured":"Pylatiuk C, Sanchez D, Mikut R, Alshut R, Reischl M, Hirth S, Rottbauer W, Just S (2014) Automatic zebrafish heartbeat detection and analysis for zebrafish embryos. Zebrafish 11(4):379\u2013383","journal-title":"Zebrafish"},{"issue":"3","key":"3444_CR15","doi-asserted-by":"publisher","first-page":"1041","DOI":"10.1109\/TASE.2017.2705240","volume":"15","author":"S Krishna","year":"2017","unstructured":"Krishna S, Chatti K, Galigekere RR (2017) Automatic and robust estimation of heart rate in zebrafish larvae. IEEE Trans Autom Sci Eng 15(3):1041\u20131052","journal-title":"IEEE Trans Autom Sci Eng"},{"issue":"1","key":"3444_CR16","doi-asserted-by":"publisher","first-page":"2046","DOI":"10.1038\/s41598-020-58563-w","volume":"10","author":"J Gierten","year":"2020","unstructured":"Gierten J, Pylatiuk C, Hammouda OT, Schock C, Stegmaier J, Wittbrodt J, Gehrig J, Loosli F (2020) Automated high-throughput heartbeat quantification in medaka and zebrafish embryos under physiological conditions. Sci Rep 10(1):2046","journal-title":"Sci Rep"},{"key":"3444_CR17","doi-asserted-by":"crossref","unstructured":"Akerberg AA, Burns CE, Burns CG, Nguyen C (2019) Deep learning enables automated volumetric assessments of cardiac function in zebrafish. Dis Model Mech 12(10):040188","DOI":"10.1242\/dmm.040188"},{"key":"3444_CR18","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, Munich, Germany, October 5\u20139, 2015, Proceedings, Part III 18, Springer, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"3444_CR19","doi-asserted-by":"crossref","unstructured":"Hoage T, Ding Y, Xu X (2012) Quantifying cardiac functions in embryonic and adult zebrafish. Cardiovascular development: methods and protocols, pp 11\u201320","DOI":"10.1007\/978-1-61779-523-7_2"},{"key":"3444_CR20","doi-asserted-by":"crossref","unstructured":"Cheng B, Misra I, Schwing AG, Kirillov A, Girdhar R (2022) Masked-attention mask transformer for universal image segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1290\u20131299","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"3444_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.envpol.2023.123149","volume":"342","author":"Y Jin","year":"2024","unstructured":"Jin Y, Shi H, Zhao Y, Dai J, Zhang K (2024) Organophosphate ester cresyl diphenyl phosphate disrupts lipid homeostasis in zebrafish embryos. Environ Pollut 342:123149","journal-title":"Environ Pollut"},{"key":"3444_CR22","doi-asserted-by":"crossref","unstructured":"Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, Xiao T, Whitehead S, Berg AC, Lo W-Y et al (2023) Segment anything. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 4015\u20134026","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"3444_CR23","unstructured":"Spiegel MR (1965) Laplace transforms. McGraw-Hill"},{"key":"3444_CR24","doi-asserted-by":"crossref","unstructured":"Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2881\u20132890","DOI":"10.1109\/CVPR.2017.660"},{"key":"3444_CR25","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"3444_CR26","doi-asserted-by":"crossref","unstructured":"Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV), pp 801\u2013818","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"3444_CR27","unstructured":"Chen L-C, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv:1706.05587"},{"key":"3444_CR28","doi-asserted-by":"crossref","unstructured":"Xiao T, Liu Y, Zhou B, Jiang Y, Sun J (2018) Unified perceptual parsing for scene understanding. In: Proceedings of the European conference on computer vision (ECCV), pp 418\u2013434","DOI":"10.1007\/978-3-030-01228-1_26"},{"key":"3444_CR29","doi-asserted-by":"crossref","unstructured":"Lin, T-Y, Doll\u00e1r, P, Girshick, R, He, K, Hariharan, B, Belongie, S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"3444_CR30","doi-asserted-by":"crossref","unstructured":"Zhao H, Qi X, Shen X, Shi J, Jia J (2018) Icnet for real-time semantic segmentation on high-resolution images. In: Proceedings of the European conference on computer vision (ECCV), pp 405\u2013420","DOI":"10.1007\/978-3-030-01219-9_25"},{"key":"3444_CR31","unstructured":"Poudel RPK, Liwicki S, Cipolla R (2019) Fast-SCNN: fast semantic segmentation network. CoRR abs\/1902.04502. arXiv:1902.04502"},{"key":"3444_CR32","unstructured":"Howard AG (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861"},{"key":"3444_CR33","doi-asserted-by":"crossref","unstructured":"Howard A, Zhmoginov A, Chen L-C, Sandler M, Zhu M (2018) Inverted residuals and linear bottlenecks: mobile networks for classification, detection and segmentation. In: Proc CVPR, pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"3444_CR34","doi-asserted-by":"crossref","unstructured":"Kirillov A, Girshick R, He K, Doll\u00e1r P (2019) Panoptic feature pyramid networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 6399\u20136408","DOI":"10.1109\/CVPR.2019.00656"},{"key":"3444_CR35","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R (2017) Mask R-CNN. In: Proceedings of the IEEE international conference on computer vision, pp 2961\u20132969","DOI":"10.1109\/ICCV.2017.322"},{"key":"3444_CR36","doi-asserted-by":"crossref","unstructured":"Zhang H, Dana K, Shi J, Zhang Z, Wang X, Tyagi A, Agrawal A (2018) Context encoding for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7151\u20137160","DOI":"10.1109\/CVPR.2018.00747"},{"key":"3444_CR37","doi-asserted-by":"crossref","unstructured":"Wang X, Girshick R, Gupta A, He K (2018) Non-local neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7794\u20137803","DOI":"10.1109\/CVPR.2018.00813"},{"key":"3444_CR38","doi-asserted-by":"crossref","unstructured":"Zhao H, Zhang Y, Liu S, Shi J, Loy CC, Lin D, Jia J (2018) Psanet: point-wise spatial attention network for scene parsing. In: Proceedings of the European conference on computer vision (ECCV), pp 267\u2013283","DOI":"10.1007\/978-3-030-01240-3_17"},{"key":"3444_CR39","doi-asserted-by":"crossref","unstructured":"Li X, Zhong Z, Wu J, Yang Y, Lin Z, Liu H (2019) Expectation-maximization attention networks for semantic segmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 9167\u20139176","DOI":"10.1109\/ICCV.2019.00926"},{"key":"3444_CR40","doi-asserted-by":"crossref","unstructured":"Cao Y, Xu J, Lin S, Wei F, Hu H (2019) GCNet: non-local networks meet squeeze-excitation networks and beyond. In: Proceedings of the IEEE\/CVF international conference on computer vision workshops, pp 0\u20130","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"3444_CR41","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"3444_CR42","doi-asserted-by":"crossref","unstructured":"Huang Z, Wang X, Huang L, Huang C, Wei Y, Liu W (2019) CCNet: criss-cross attention for semantic segmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 603\u2013612","DOI":"10.1109\/ICCV.2019.00069"},{"key":"3444_CR43","doi-asserted-by":"crossref","unstructured":"He J, Deng Z, Qiao Y (2019) Dynamic multi-scale filters for semantic segmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 3562\u20133572","DOI":"10.1109\/ICCV.2019.00366"},{"key":"3444_CR44","doi-asserted-by":"crossref","unstructured":"Fu J, Liu J, Tian H, Li Y, Bao Y, Fang Z, Lu H (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3146\u20133154","DOI":"10.1109\/CVPR.2019.00326"},{"key":"3444_CR45","doi-asserted-by":"crossref","unstructured":"Yuan, Y, Chen, X, Wang J (2020) Object-contextual representations for semantic segmentation. In: Computer vision\u2013ECCV 2020: 16th European conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part VI 16, Springer, pp 173\u2013190","DOI":"10.1007\/978-3-030-58539-6_11"},{"key":"3444_CR46","doi-asserted-by":"crossref","unstructured":"Yin M, Yao Z, Cao Y, Li X, Zhang Z, Lin S, Hu H (2020) Disentangled non-local neural networks. In: Computer vision\u2013ECCV 2020: 16th European conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XV 16, Springer, pp 191\u2013207","DOI":"10.1007\/978-3-030-58555-6_12"},{"key":"3444_CR47","first-page":"12077","volume":"34","author":"E Xie","year":"2021","unstructured":"Xie E, Wang W, Yu Z, Anandkumar A, Alvarez JM, Luo P (2021) Segformer: simple and efficient design for semantic segmentation with transformers. Adv Neural Inf Process Syst 34:12077\u201312090","journal-title":"Adv Neural Inf Process Syst"},{"key":"3444_CR48","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30"},{"key":"3444_CR49","first-page":"10326","volume":"34","author":"W Zhang","year":"2021","unstructured":"Zhang W, Pang J, Chen K, Loy CC (2021) K-Net: towards unified image segmentation. Adv Neural Inf Process Syst 34:10326\u201310338","journal-title":"Adv Neural Inf Process Syst"},{"key":"3444_CR50","unstructured":"Cheng B, Schwing A, Kirillov A (2021) Per-pixel classification is not all you need for semantic segmentation. Adv Neural Inf Process Syst 34L17864\u201317875"},{"issue":"1","key":"3444_CR51","doi-asserted-by":"publisher","first-page":"8","DOI":"10.3390\/inventions6010008","volume":"6","author":"KA Kurnia","year":"2021","unstructured":"Kurnia KA, Saputra F, Roldan MJM, Castillo AL, Huang J-C, Chen KH-C, Lai H-T, Hsiao C-D (2021) Measurement of multiple cardiac performance endpoints in daphnia and zebrafish by kymograph. Inventions 6(1):8","journal-title":"Inventions"},{"issue":"8","key":"3444_CR52","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.3390\/biology11081243","volume":"11","author":"ME Suryanto","year":"2022","unstructured":"Suryanto ME, Saputra F, Kurnia KA, Vasquez RD, Roldan MJM, Chen KH-C, Huang J-C, Hsiao C-D (2022) Using deeplabcut as a real-time and markerless tool for cardiac physiology assessment in zebrafish. Biology 11(8):1243","journal-title":"Biology"},{"key":"3444_CR53","unstructured":"Contributors M (2020) Mmsegmentation, an open source semantic segmentation toolbox"},{"key":"3444_CR54","doi-asserted-by":"crossref","unstructured":"Jadon S (2020) A survey of loss functions for semantic segmentation. In: 2020 IEEE Conference on computational intelligence in bioinformatics and computational biology (CIBCB), IEEE, pp 1\u20137","DOI":"10.1109\/CIBCB48159.2020.9277638"},{"issue":"7","key":"3444_CR55","doi-asserted-by":"publisher","first-page":"1723","DOI":"10.1007\/s11517-023-02814-1","volume":"61","author":"V Punyaprabha","year":"2023","unstructured":"Punyaprabha V, Sushma T, Suresh S (2023) Performance evaluation of computer-aided automated master frame selection techniques for fetal echocardiography. Med Biol Eng Comput 61(7):1723\u20131744","journal-title":"Med Biol Eng Comput"},{"key":"3444_CR56","doi-asserted-by":"crossref","unstructured":"Xu H, Berendt R, Jha N, Mandal M (2017) Automatic measurement of melanoma depth of invasion in skin histopathological images. Micron 97:56\u201367","DOI":"10.1016\/j.micron.2017.03.004"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03444-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-025-03444-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03444-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T05:57:12Z","timestamp":1770098232000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-025-03444-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,16]]},"references-count":56,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["3444"],"URL":"https:\/\/doi.org\/10.1007\/s11517-025-03444-5","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,16]]},"assertion":[{"value":"12 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All animal experiments in this study comply with the Guiding Principles in the Care and Use of Animals.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}