{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T16:42:47Z","timestamp":1790700167966,"version":"4.1.0"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,27]],"date-time":"2025-12-27T00:00:00Z","timestamp":1766793600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T00:00:00Z","timestamp":1768780800000},"content-version":"vor","delay-in-days":23,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/100000053","name":"U.S. Department of Health & Human Services | NIH | National Eye Institute","doi-asserted-by":"publisher","award":["R15EY035804"],"award-info":[{"award-number":["R15EY035804"]}],"id":[{"id":"10.13039\/100000053","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000053","name":"U.S. Department of Health & Human Services | NIH | National Eye Institute","doi-asserted-by":"publisher","award":["R21EY035271"],"award-info":[{"award-number":["R21EY035271"]}],"id":[{"id":"10.13039\/100000053","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010942","name":"UNC | University of North Carolina at Charlotte","doi-asserted-by":"publisher","award":["AI4Health seed garnt"],"award-info":[{"award-number":["AI4Health seed garnt"]}],"id":[{"id":"10.13039\/100010942","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Commun Med"],"DOI":"10.1038\/s43856-025-01293-9","type":"journal-article","created":{"date-parts":[[2025,12,27]],"date-time":"2025-12-27T12:45:12Z","timestamp":1766839512000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Compact vision language models enable efficient and interpretable optical coherence tomography through layer-specific multimodal learning"],"prefix":"10.1038","volume":"6","author":[{"given":"Tania","family":"Haghighi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sina","family":"Gholami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jared Todd","family":"Sokol","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aayush","family":"Biswas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer I.","family":"Lim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8461-3562","authenticated-orcid":false,"given":"Theodore","family":"Leng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atalie C.","family":"Thompson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamed","family":"Tabkhi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3095-2232","authenticated-orcid":false,"given":"Minhaj Nur","family":"Alam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,27]]},"reference":[{"key":"1293_CR1","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1038\/s44172-025-00341-5","volume":"4","author":"S Gholami","year":"2025","unstructured":"Gholami, S. et al. Distributed training of foundation models for ophthalmic diagnosis. Commun. Eng. 4, 6 (2025).","journal-title":"Commun. Eng."},{"key":"1293_CR2","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1038\/s41586-023-06555-x","volume":"622","author":"Y Zhou","year":"2023","unstructured":"Zhou, Y. et al. A foundation model for generalizable disease detection from retinal images. Nature 622, 156\u2013163 (2023).","journal-title":"Nature"},{"key":"1293_CR3","doi-asserted-by":"crossref","unstructured":"Li, Z. et al. VisionUnite: a vision\u2013language foundation model for ophthalmology enhanced with clinical knowledge. IEEE Trans. Pattern Anal. Mach. Intell. 47, 11848\u201311862 (2025).","DOI":"10.1109\/TPAMI.2025.3598734"},{"key":"1293_CR4","doi-asserted-by":"crossref","unstructured":"Wang, C., Chen, X., Ning, H. & Li, S. Sam-octa: a fine-tuning strategy for applying foundation model octa image segmentation tasks. In Proc. ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 1771\u20131775 (IEEE, 2024).","DOI":"10.1109\/ICASSP48485.2024.10446904"},{"key":"1293_CR5","doi-asserted-by":"publisher","first-page":"1178","DOI":"10.1126\/science.1957169","volume":"254","author":"D Huang","year":"1991","unstructured":"Huang, D. et al. Optical coherence tomography. Science 254, 1178\u20131181 (1991).","journal-title":"Science"},{"key":"1293_CR6","doi-asserted-by":"publisher","first-page":"2489","DOI":"10.1016\/j.ophtha.2014.07.054","volume":"121","author":"PA Keane","year":"2014","unstructured":"Keane, P. A. & Sadda, S. R. Retinal imaging in the twenty-first century: state of the art and future directions. Ophthalmology 121, 2489\u20132500 (2014).","journal-title":"Ophthalmology"},{"key":"1293_CR7","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.preteyeres.2007.07.005","volume":"27","author":"W Drexler","year":"2008","unstructured":"Drexler, W. & Fujimoto, J. G. State-of-the-art retinal optical coherence tomography. Prog. Retin. Eye Res. 27, 45\u201388 (2008).","journal-title":"Prog. Retin. Eye Res."},{"key":"1293_CR8","doi-asserted-by":"publisher","first-page":"1260","DOI":"10.1016\/S0161-6420(96)30512-5","volume":"103","author":"MR Hee","year":"1996","unstructured":"Hee, M. R. et al. Optical coherence tomography of age-related macular degeneration and choroidal neovascularization. Ophthalmology 103, 1260\u20131270 (1996).","journal-title":"Ophthalmology"},{"key":"1293_CR9","doi-asserted-by":"publisher","first-page":"3453","DOI":"10.3390\/app13063453","volume":"13","author":"H Shin","year":"2023","unstructured":"Shin, H., Jeon, S., Seol, Y., Kim, S. & Kang, D. Vision transformer approach for classification of Alzheimer\u2019s disease using 18F-florbetaben brain images. Appl. Sci. 13, 3453 (2023).","journal-title":"Appl. Sci."},{"key":"1293_CR10","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1038\/s41591-018-0107-6","volume":"24","author":"J De Fauw","year":"2018","unstructured":"De Fauw, J. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24, 1342\u20131350 (2018).","journal-title":"Nat. Med."},{"key":"1293_CR11","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","volume":"172","author":"DS Kermany","year":"2018","unstructured":"Kermany, D. S. et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172, 1122\u20131131 (2018).","journal-title":"Cell"},{"key":"1293_CR12","doi-asserted-by":"crossref","unstructured":"Subhedar, J. & Mahajan, A. A review on recent work on oct image classification for disease detection. In Proc. 2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development (OTCON) 1\u20136 (IEEE, 2023).","DOI":"10.1109\/OTCON56053.2023.10114003"},{"key":"1293_CR13","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1016\/j.ophtha.2017.10.031","volume":"125","author":"T Schlegl","year":"2018","unstructured":"Schlegl, T. et al. Fully automated detection and quantification of macular fluid in OCT using deep learning. Ophthalmology 125, 549\u2013558 (2018).","journal-title":"Ophthalmology"},{"key":"1293_CR14","doi-asserted-by":"publisher","first-page":"2318","DOI":"10.1167\/iovs.16-20541","volume":"58","author":"FG Venhuizen","year":"2017","unstructured":"Venhuizen, F. G. et al. Automated staging of age-related macular degeneration using optical coherence tomography. Investig. Ophthalmol. Vis. Sci. 58, 2318\u20132328 (2017).","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"1293_CR15","doi-asserted-by":"publisher","first-page":"3627","DOI":"10.1364\/BOE.8.003627","volume":"8","author":"AG Roy","year":"2017","unstructured":"Roy, A. G. et al. Relaynet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomed. Opt. Express 8, 3627\u20133642 (2017).","journal-title":"Biomed. Opt. Express"},{"key":"1293_CR16","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1167\/tvst.9.2.61","volume":"9","author":"Q Li","year":"2020","unstructured":"Li, Q. et al. Deepretina: layer segmentation of retina in oct images using deep learning. Transl. Vis. Sci. Technol. 9, 61\u201361 (2020).","journal-title":"Transl. Vis. Sci. Technol."},{"key":"1293_CR17","doi-asserted-by":"crossref","unstructured":"Apostolopoulos, S., De Zanet, S., Ciller, C., Wolf, S. & Sznitman, R. Pathological OCT retinal layer segmentation using branch residual U-shape networks. In Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention 294\u2013301 (Springer, 2017).","DOI":"10.1007\/978-3-319-66179-7_34"},{"key":"1293_CR18","doi-asserted-by":"publisher","unstructured":"He, Y. et al. Topology guaranteed segmentation of the human retina from oct using convolutional neural networks. Preprint at https:\/\/doi.org\/10.48550\/arXiv.1803.05120 (2018).","DOI":"10.48550\/arXiv.1803.05120"},{"key":"1293_CR19","doi-asserted-by":"crossref","unstructured":"Jing, B., Xie, P. & Xing, E. On the automatic generation of medical imaging reports. Proc. 56th Annu. Meet. Assoc. Comput. Linguist. 1, 2577\u20132586 (2018).","DOI":"10.18653\/v1\/P18-1240"},{"key":"1293_CR20","doi-asserted-by":"crossref","unstructured":"Li, M. et al. Cross-modal clinical graph transformer for ophthalmic report generation. In Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition 20656\u201320665 (IEEE, 2022).","DOI":"10.1109\/CVPR52688.2022.02000"},{"key":"1293_CR21","doi-asserted-by":"crossref","unstructured":"Huang, J.-H. et al. Deepopht: medical report generation for retinal images via deep models and visual explanation. In Proc. Winter Conference on Applications of Computer Vision 2442\u20132452 (IEEE, 2021).","DOI":"10.1109\/WACV48630.2021.00249"},{"key":"1293_CR22","doi-asserted-by":"publisher","unstructured":"Limbu, M. & Banerjee, D. Medblip: fine-tuning blip for medical image captioning. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2505.14726 (2025).","DOI":"10.48550\/arXiv.2505.14726"},{"key":"1293_CR23","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1038\/s41591-024-03302-1","volume":"31","author":"R Tanno","year":"2025","unstructured":"Tanno, R. et al. Collaboration between clinicians and vision\u2013language models in radiology report generation. Nat. Med. 31, 599\u2013608 (2025).","journal-title":"Nat. Med."},{"key":"1293_CR24","doi-asserted-by":"crossref","unstructured":"Holland, R. et al. Specialized curricula for training vision\u2013language models in retinal image analysis. NPJ Digit. Med. 8, 532 (2025).","DOI":"10.1038\/s41746-025-01893-8"},{"key":"1293_CR25","doi-asserted-by":"publisher","first-page":"100662","DOI":"10.1016\/j.xops.2024.100662","volume":"5","author":"EW Schneider","year":"2025","unstructured":"Schneider, E. W. et al. Pivotal trial toward effectiveness of self-administered oct in neovascular age-related macular degeneration. report 2-artificial intelligence analytics. Ophthalmol. Sci. 5, 100662 (2025).","journal-title":"Ophthalmol. Sci."},{"key":"1293_CR26","first-page":"4","volume":"11","author":"X Liu","year":"2022","unstructured":"Liu, X. et al. Evaluation of an oct-AI\u2013based telemedicine platform for retinal disease screening and referral in a primary care setting. Transl. Vis. Sci. Technol. 11, 4\u20134 (2022).","journal-title":"Transl. Vis. Sci. Technol."},{"key":"1293_CR27","first-page":"28541","volume":"36","author":"C Li","year":"2023","unstructured":"Li, C. et al. Llava-med: training a large language-and-vision assistant for biomedicine in one day. Adv. Neural Inf. Process. Syst. 36, 28541\u201328564 (2023).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1293_CR28","doi-asserted-by":"crossref","unstructured":"Haghighi, T. et al. EYE-Llama: an in-domain large language model for ophthalmology. iScience 28 (2025).","DOI":"10.1016\/j.isci.2025.112984"},{"key":"1293_CR29","unstructured":"OpenAI. Gpt-4o system card (2024). Accessed: 2025-07-17."},{"key":"1293_CR30","unstructured":"Li, J., Li, D., Xiong, C. & Hoi, S. Blip: bootstrapping language-image pre-training for unified vision-language understanding and generation. In Proc. International Conference On Machine Learning 12888\u201312900 (PMLR, 2022)."},{"key":"1293_CR31","doi-asserted-by":"publisher","unstructured":"Wang, J. et al. Git: a generative image-to-text transformer for vision and language. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2205.14100 (2022).","DOI":"10.48550\/arXiv.2205.14100"},{"key":"1293_CR32","doi-asserted-by":"publisher","unstructured":"Steiner, A. et al. Paligemma 2: a family of versatile vlms for transfer. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2412.03555 (2024).","DOI":"10.48550\/arXiv.2412.03555"},{"key":"1293_CR33","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1007\/s10103-024-04089-w","volume":"39","author":"S Ak\u00e7a","year":"2024","unstructured":"Ak\u00e7a, S., Garip, Z., Ekinci, E. & Atban, F. Automated classification of choroidal neovascularization, diabetic macular edema, and drusen from retinal oct images using vision transformers: a comparative study. Lasers Med. Sci. 39, 140 (2024).","journal-title":"Lasers Med. Sci."},{"key":"1293_CR34","doi-asserted-by":"publisher","unstructured":"Simonyan, K., Vedaldi, A. & Zisserman, A. Deep inside convolutional networks: Visualising image classification models and saliency maps. Preprint at https:\/\/doi.org\/10.48550\/arXiv.1312.6034 (2013).","DOI":"10.48550\/arXiv.1312.6034"},{"key":"1293_CR35","doi-asserted-by":"crossref","unstructured":"Selvaraju, R. R. et al. Grad-cam: visual explanations from deep networks via gradient-based localization. In Proc. IEEE International Conference on Computer Vision 618\u2013626 (IEEE, 2017).","DOI":"10.1109\/ICCV.2017.74"},{"key":"1293_CR36","doi-asserted-by":"publisher","first-page":"3568","DOI":"10.1364\/BOE.5.003568","volume":"5","author":"PP Srinivasan","year":"2014","unstructured":"Srinivasan, P. P. et al. Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. Biomed. Opt. Express 5, 3568\u20133577 (2014).","journal-title":"Biomed. Opt. Express"},{"key":"1293_CR37","doi-asserted-by":"publisher","unstructured":"Reimers, N. & Gurevych, I. Sentence-bert: sentence embeddings using siamese bert-networks. Preprint at https:\/\/doi.org\/10.48550\/arXiv.1908.10084 (2019).","DOI":"10.48550\/arXiv.1908.10084"},{"key":"1293_CR38","unstructured":"Chakraborty, A. Medembed: Large biomedical embeddings for semantic search. https:\/\/huggingface.co\/abhinand\/MedEmbed-large-v0.1 (2024)."},{"key":"1293_CR39","doi-asserted-by":"publisher","unstructured":"Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q. & Artzi, Y. Bertscore: evaluating text generation with Bert. Preprint at https:\/\/doi.org\/10.48550\/arXiv.1904.09675 (2019).","DOI":"10.48550\/arXiv.1904.09675"},{"key":"1293_CR40","doi-asserted-by":"crossref","unstructured":"Papineni, K., Roukos, S., Ward, T. & Zhu, W.-J. Bleu: a method for automatic evaluation of machine translation. In Proc. 40th Annual Meeting of the Association for Computational Linguistics 311\u2013318 (ACM, 2002).","DOI":"10.3115\/1073083.1073135"},{"key":"1293_CR41","doi-asserted-by":"crossref","unstructured":"Chen, B. & Cherry, C. A systematic comparison of smoothing techniques for sentence-level BLEU. In Proc. 9th Workshop on Statistical Machine Translation 362\u2013367 (2014).","DOI":"10.3115\/v1\/W14-3346"},{"key":"1293_CR42","unstructured":"Lin, C.-Y. Rouge: a package for automatic evaluation of summaries. In Text summarization branches out, 74\u201381 (2004)."},{"key":"1293_CR43","doi-asserted-by":"crossref","unstructured":"Vedantam, R., Lawrence Zitnick, C. & Parikh, D. Cider: Consensus-based image description evaluation. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 4566\u20134575 (IEEE, 2015).","DOI":"10.1109\/CVPR.2015.7299087"},{"key":"1293_CR44","doi-asserted-by":"crossref","unstructured":"He, K. et al. Masked autoencoders are scalable vision learners. In Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition 16000\u201316009 (IEEE, 2022).","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"1293_CR45","doi-asserted-by":"crossref","unstructured":"Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies Volume 1 (long and short papers) 4171\u20134186 (2019).","DOI":"10.18653\/v1\/N19-1423"},{"key":"1293_CR46","first-page":"3","volume":"1","author":"EJ Hu","year":"2022","unstructured":"Hu, E. J. et al. Lora: Low-rank adaptation of large language models. ICLR 1, 3 (2022).","journal-title":"ICLR"},{"key":"1293_CR47","doi-asserted-by":"publisher","first-page":"103357","DOI":"10.1016\/j.media.2024.103357","volume":"99","author":"J Silva-Rodriguez","year":"2025","unstructured":"Silva-Rodriguez, J., Chakor, H., Kobbi, R., Dolz, J. & Ayed, I. B. A foundation language-image model of the retina (flair): encoding expert knowledge in text supervision. Med. Image Anal. 99, 103357 (2025).","journal-title":"Med. Image Anal."},{"key":"1293_CR48","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01101-z","volume":"7","author":"X Chen","year":"2024","unstructured":"Chen, X. et al. Ffa-gpt: an automated pipeline for fundus fluorescein angiography interpretation and question-answer. npj Digit. Med. 7, 111 (2024).","journal-title":"npj Digit. Med."},{"key":"1293_CR49","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-01759-z","volume":"8","author":"A Shao","year":"2025","unstructured":"Shao, A. et al. Generative artificial intelligence for fundus fluorescein angiography interpretation and human expert evaluation. npj Digit. Med. 8, 396 (2025).","journal-title":"npj Digit. Med."},{"key":"1293_CR50","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-60577-9","volume":"16","author":"M Wang","year":"2025","unstructured":"Wang, M. et al. Enhancing diagnostic accuracy in rare and common fundus diseases with a knowledge-rich vision-language model. Nat. Commun. 16, 5528 (2025).","journal-title":"Nat. Commun."},{"key":"1293_CR51","doi-asserted-by":"publisher","unstructured":"Shi, D. et al. Eyeclip: a visual-language foundation model for multi-modal ophthalmic image analysis. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2409.06644 (2024).","DOI":"10.48550\/arXiv.2409.06644"},{"key":"1293_CR52","doi-asserted-by":"publisher","unstructured":"Wu, R. et al. Mm-retinal v2: transfer an elite knowledge spark into fundus vision-language pretraining. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2501.15798 (2025).","DOI":"10.48550\/arXiv.2501.15798"},{"key":"1293_CR53","doi-asserted-by":"crossref","unstructured":"Cherukuri, T. K., Shaik, N. S., Bodapati, J. D. & Ye, D. H. Gcs-m3vlt: guided context self-attention based multi-modal medical vision language transformer for retinal image captioning. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 1\u20135 (IEEE, 2025).","DOI":"10.1109\/ICASSP49660.2025.10890310"},{"key":"1293_CR54","unstructured":"QIAIUNCC. Oct-summary-dataset. https:\/\/huggingface.co\/datasets\/QIAIUNCC\/OCT-summary-Dataset (2025)."},{"key":"1293_CR55","doi-asserted-by":"publisher","first-page":"106532","DOI":"10.1016\/j.compeleceng.2019.106532","volume":"81","author":"P Gholami","year":"2020","unstructured":"Gholami, P., Roy, P., Parthasarathy, M. K. & Lakshminarayanan, V. Octid: optical coherence tomography image database. Comput. Electr. Eng. 81, 106532 (2020).","journal-title":"Comput. Electr. Eng."},{"key":"1293_CR56","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-024-03182-7","volume":"11","author":"M Kulyabin","year":"2024","unstructured":"Kulyabin, M. et al. Octdl: optical coherence tomography dataset for image-based deep learning methods. Sci. Data 11, 365 (2024).","journal-title":"Sci. Data"},{"key":"1293_CR57","unstructured":"Haghighi, T. et al. Lo-vlm: Layer-wise oct vlm github repository. https:\/\/github.com\/QIAIUNCC\/LO-VLM (2025)."}],"container-title":["Communications Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s43856-025-01293-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43856-025-01293-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43856-025-01293-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T16:47:12Z","timestamp":1768841232000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s43856-025-01293-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,27]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1293"],"URL":"https:\/\/doi.org\/10.1038\/s43856-025-01293-9","relation":{},"ISSN":["2730-664X"],"issn-type":[{"value":"2730-664X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,27]]},"assertion":[{"value":"12 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 December 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":"32"}}