{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T23:27:39Z","timestamp":1786404459210,"version":"build-2736575974"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T00:00:00Z","timestamp":1780099200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T00:00:00Z","timestamp":1786406400000},"content-version":"vor","delay-in-days":73,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-55088-6","type":"journal-article","created":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T08:03:52Z","timestamp":1780128232000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A lightweight deep learning model with channel attention for kidney cell classification from microscopy images"],"prefix":"10.1038","volume":"16","author":[{"given":"Mithila","family":"Arman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Mahid Arfan","family":"Rahat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahabuba Akter","family":"Thithi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johir Uddin","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shahriar Mahmud","family":"Kabir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Imamul","family":"Islam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehedi","family":"Hasan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed Nazmus","family":"Shakib","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heng Siong","family":"Lim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,30]]},"reference":[{"key":"55088_CR1","doi-asserted-by":"publisher","first-page":"5161","DOI":"10.1038\/s41467-025-60435-8","volume":"16","author":"L McLaughlin","year":"2025","unstructured":"McLaughlin, L. et al. Three dimensional multiscalar neurovascular nephron connectivity map of the human kidney across the lifespan. Nature communications 16, 5161 (2025).","journal-title":"Nature communications"},{"key":"55088_CR2","doi-asserted-by":"publisher","first-page":"1508806","DOI":"10.3389\/fphys.2024.1508806","volume":"15","author":"D Maskey","year":"2024","unstructured":"Maskey, D., Granados Pineda, J. & Ortiz, P. A. Update on nkcc2 regulation in the thick ascending limb (tal) by membrane trafficking, phosphorylation, and protein-protein interactions. Frontiers in Physiology 15, 1508806 (2024).","journal-title":"Frontiers in Physiology"},{"key":"55088_CR3","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1038\/s41586-023-05769-3","volume":"619","author":"BB Lake","year":"2023","unstructured":"Lake, B. B. et al. An atlas of healthy and injured cell states and niches in the human kidney. Nature 619, 585\u2013594 (2023).","journal-title":"Nature"},{"key":"55088_CR4","doi-asserted-by":"publisher","first-page":"eadv8918","DOI":"10.1126\/sciadv.adv8918","volume":"11","author":"M Asghari","year":"2025","unstructured":"Asghari, M. et al. Integration of spatial protein imaging and transcriptomics in the human kidney tracks the regenerative potential of proximal tubules. Science Advances 11, eadv8918 (2025).","journal-title":"Science Advances"},{"key":"55088_CR5","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1093\/ndt\/gfz136","volume":"36","author":"ML Angelotti","year":"2021","unstructured":"Angelotti, M. L., Antonelli, G., Conte, C. & Romagnani, P. Imaging the kidney: From light to super-resolution microscopy. Nephrol. Dial. Transplant. 36, 19\u201328 (2021).","journal-title":"Nephrol. Dial. Transplant."},{"key":"55088_CR6","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1186\/s40779-025-00680-6","volume":"12","author":"X-M Zhang","year":"2025","unstructured":"Zhang, X.-M. et al. Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects. Military Medical Research 12, 93 (2025).","journal-title":"Military Medical Research"},{"key":"55088_CR7","doi-asserted-by":"publisher","first-page":"26748","DOI":"10.1038\/s41598-024-76807-x","volume":"14","author":"C Greeley","year":"2024","unstructured":"Greeley, C., Holder, L., Nilsson, E. E. & Skinner, M. K. Scalable deep learning artificial intelligence histopathology slide analysis and validation. Scientific Reports 14, 26748 (2024).","journal-title":"Scientific Reports"},{"key":"55088_CR8","doi-asserted-by":"publisher","first-page":"20936","DOI":"10.1038\/s41598-024-69024-z","volume":"14","author":"J Sasikumar","year":"2024","unstructured":"Sasikumar, J., Laha, S., Naik, B. & Das, S. P. Enhanced visualization of nuclear staining and cell cycle analysis for the human commensal malassezia. Scientific Reports 14, 20936 (2024).","journal-title":"Scientific Reports"},{"key":"55088_CR9","doi-asserted-by":"publisher","DOI":"10.1126\/science.adk9217","volume":"385","author":"L Sundaram","year":"2024","unstructured":"Sundaram, L. et al. Single-cell chromatin accessibility reveals malignant regulatory programs in primary human cancers. Science 385, eadk9217 (2024).","journal-title":"Science"},{"key":"55088_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2024.111434","author":"G Comolet","year":"2024","unstructured":"Comolet, G. et al. A highly efficient, scalable pipeline for fixed feature extraction from large-scale high-content imaging screens. iScience https:\/\/doi.org\/10.1016\/j.isci.2024.111434 (2024).","journal-title":"iScience"},{"key":"55088_CR11","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1002\/jim4.17","volume":"1","author":"J Mao","year":"2024","unstructured":"Mao, J. & He, H. Deep learning in fluorescence imaging and analysis. J. Intell. Med. 1, 42\u201362 (2024).","journal-title":"J. Intell. Med."},{"key":"55088_CR12","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1109\/JPROC.2021.3054390","volume":"109","author":"SK Zhou","year":"2021","unstructured":"Zhou, S. K. et al. A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises. Proc. IEEE. 109, 820\u2013838 (2021).","journal-title":"Proc. IEEE."},{"key":"55088_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12065-020-00540-3","volume":"15","author":"D Sarvamangala","year":"2022","unstructured":"Sarvamangala, D. & Kulkarni, R. V. Convolutional neural networks in medical image understanding: a survey. Evolutionary intelligence 15, 1\u201322 (2022).","journal-title":"Evolutionary intelligence"},{"key":"55088_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102762","volume":"85","author":"J Li","year":"2023","unstructured":"Li, J. et al. Transforming medical imaging with transformers? a comparative review of key properties, current progresses, and future perspectives. Medical image analysis 85, 102762 (2023).","journal-title":"Medical image analysis"},{"key":"55088_CR15","doi-asserted-by":"publisher","first-page":"8999","DOI":"10.3390\/s22228999","volume":"22","author":"YA Kadhim","year":"2022","unstructured":"Kadhim, Y. A., Khan, M. U. & Mishra, A. Deep learning-based computer-aided diagnosis (cad): Applications for medical image datasets. Sensors 22, 8999 (2022).","journal-title":"Sensors"},{"key":"55088_CR16","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-33178-1","author":"MMA Rahat","year":"2026","unstructured":"Rahat, M. M. A., Islam, M. I., Miah, M. S. U. & Alharbi, T. An efficient dual path deep learning framework for COVID-19 classification using lung CT scans with explainable AI. Sci. Rep. https:\/\/doi.org\/10.1038\/s41598-025-33178-1 (2026).","journal-title":"Sci. Rep."},{"key":"55088_CR17","doi-asserted-by":"crossref","unstructured":"Yang, J., Shi, R. & Ni, B. Medmnist classification decathlon: A lightweight automl benchmark for medical image analysis. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 191\u2013195 (IEEE, 2021).","DOI":"10.1109\/ISBI48211.2021.9434062"},{"key":"55088_CR18","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1038\/s41597-022-01721-8","volume":"10","author":"J Yang","year":"2023","unstructured":"Yang, J. et al. Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification. Scientific Data 10, 41 (2023).","journal-title":"Scientific Data"},{"key":"55088_CR19","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1008179","volume":"16","author":"N Korfhage","year":"2020","unstructured":"Korfhage, N. et al. Detection and segmentation of morphologically complex eukaryotic cells in fluorescence microscopy images via feature pyramid fusion. PLoS computational biology 16, e1008179 (2020).","journal-title":"PLoS computational biology"},{"key":"55088_CR20","doi-asserted-by":"publisher","first-page":"7669","DOI":"10.1038\/s41598-025-92156-9","volume":"15","author":"S Doerrich","year":"2025","unstructured":"Doerrich, S., Di Salvo, F., Brockmann, J. & Ledig, C. Rethinking model prototyping through the medmnist+ dataset collection. Scientific reports 15, 7669 (2025).","journal-title":"Scientific reports"},{"key":"55088_CR21","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1002\/cyto.a.24274","volume":"99","author":"A Woloshuk","year":"2021","unstructured":"Woloshuk, A. et al. In situ classification of cell types in human kidney tissue using 3d nuclear staining. Cytometry A. 99, 707\u2013721 (2021).","journal-title":"Cytometry A."},{"key":"55088_CR22","doi-asserted-by":"publisher","unstructured":"Yang, J. et al. [medmnist+] 18x standardized datasets for 2d and 3d biomedical image classification with multiple size options: 28 (mnist-like), 64, 128, and 224, https:\/\/doi.org\/10.5281\/zenodo.10519652 (2024).","DOI":"10.5281\/zenodo.10519652"},{"key":"55088_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106791","volume":"157","author":"ON Manzari","year":"2023","unstructured":"Manzari, O. N., Ahmadabadi, H., Kashiani, H., Shokouhi, S. B. & Ayatollahi, A. Medvit: a robust vision transformer for generalized medical image classification. Computers in biology and medicine 157, 106791 (2023).","journal-title":"Computers in biology and medicine"},{"key":"55088_CR24","doi-asserted-by":"crossref","unstructured":"Manzari, O.\u00a0N., Asgariandehkordi, H., Koleilat, T., Xiao, Y. & Rivaz, H. Medical image classification with kan-integrated transformers and dilated neighborhood attention. Applied Soft Computing 114045 (2025).","DOI":"10.1016\/j.asoc.2025.114045"},{"key":"55088_CR25","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0309368","volume":"19","author":"J Liu","year":"2024","unstructured":"Liu, J., Fan, K., Cai, X. & Niranjan, M. Few-shot learning for inference in medical imaging with subspace feature representations. PLoS One 19, e0309368 (2024).","journal-title":"PLoS One"},{"key":"55088_CR26","unstructured":"Bundele, V. Evaluating self-supervised learning in medical imaging: A benchmark for robustness, generalizability, and multi-domain impact (2025) arXiv:2412.19124."},{"key":"55088_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10489-025-06967-4","volume":"55","author":"S Sun","year":"2025","unstructured":"Sun, S., Jia, X. & Zheng, Z. Complex mixer for MedMNIST classification decathlon. Appl. Intell. 55, 1\u201312 (2025).","journal-title":"Appl. Intell."},{"key":"55088_CR28","doi-asserted-by":"publisher","first-page":"28585","DOI":"10.1038\/s41598-025-10712-9","volume":"15","author":"AK Chanchal","year":"2025","unstructured":"Chanchal, A. K., Lal, S. & Suresh, S. Development and evaluation of deep neural networks for the classification of subtypes of renal cell carcinoma from kidney histopathology images. Scientific Reports 15, 28585 (2025).","journal-title":"Scientific Reports"},{"key":"55088_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.labinv.2023.100104","volume":"103","author":"S Winfree","year":"2023","unstructured":"Winfree, S. et al. Integrated cytometry with machine learning applied to high-content imaging of human kidney tissue for in situ cell classification and neighborhood analysis. Laboratory Investigation 103, 100104 (2023).","journal-title":"Laboratory Investigation"},{"key":"55088_CR30","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.1038\/s42256-024-00883-x","volume":"6","author":"D Carnevali","year":"2024","unstructured":"Carnevali, D. et al. A deep learning method that identifies cellular heterogeneity using nanoscale nuclear features. Nat. Mach. Intell. 6, 1021\u20131033 (2024).","journal-title":"Nat. Mach. Intell."},{"key":"55088_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.109052","volume":"181","author":"M Mamalakis","year":"2024","unstructured":"Mamalakis, M., Macfarlane, S. C., Notley, S. V., Gad, A. K. & Panoutsos, G. A novel pipeline employing deep multi-attention channels network for the autonomous detection of metastasizing cells through fluorescence microscopy. Computers in Biology and Medicine 181, 109052 (2024).","journal-title":"Computers in Biology and Medicine"},{"key":"55088_CR32","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1186\/s12864-025-12194-5","volume":"26","author":"X An","year":"2025","unstructured":"An, X. et al. Proteinformer: protein subcellular localization based on bioimages and modified pre-trained transformer. BMC genomics 26, 1009 (2025).","journal-title":"BMC genomics"},{"key":"55088_CR33","doi-asserted-by":"publisher","first-page":"219","DOI":"10.3390\/jimaging11070219","volume":"11","author":"Z Liu","year":"2025","unstructured":"Liu, Z., Wu, P., Zhao, Z. & Yu, H. Ilvit: An inception-linear attention-based lightweight vision transformer for microscopic cell classification. Journal of Imaging 11, 219 (2025).","journal-title":"Journal of Imaging"},{"key":"55088_CR34","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/s13755-025-00425-8","volume":"14","author":"H Yee","year":"2026","unstructured":"Yee, H. et al. A data fusion deep learning approach for accurate organelle-based classification of cancer cells. Health Information Science and Systems 14, 37 (2026).","journal-title":"Health Information Science and Systems"},{"key":"55088_CR35","doi-asserted-by":"publisher","first-page":"41936","DOI":"10.1038\/s41598-025-25857-w","volume":"15","author":"M Ferdous","year":"2025","unstructured":"Ferdous, M., Mahmud, S. & Shimul, M. E. Z. Mednet: a lightweight attention-augmented cnn for medical image classification. Scientific Reports 15, 41936 (2025).","journal-title":"Scientific Reports"},{"key":"55088_CR36","doi-asserted-by":"crossref","unstructured":"Gomaa, A. & Saad, O.\u00a0M. Scar-net: A selective channel attention with residuals network for high-resolution remote sensing scene classification. In 2025 7th Novel Intelligent and Leading Emerging Sciences Conference (NILES), 522\u2013525 (IEEE, 2025).","DOI":"10.1109\/NILES68063.2025.11232133"},{"key":"55088_CR37","doi-asserted-by":"publisher","first-page":"275","DOI":"10.3390\/jimaging11080275","volume":"11","author":"AKB Arnob","year":"2025","unstructured":"Arnob, A. K. B., Chayon, M. H. R., Al Farid, F., Husen, M. N. & Ahmed, F. A lightweight cnn for multiclass retinal disease screening with explainable ai. Journal of Imaging 11, 275 (2025).","journal-title":"Journal of Imaging"},{"key":"55088_CR38","doi-asserted-by":"publisher","first-page":"6116","DOI":"10.1038\/s41598-025-90418-0","volume":"15","author":"H Liu","year":"2025","unstructured":"Liu, H. et al. A skin disease classification model based on multi scale combined efficient channel attention module. Scientific Reports 15, 6116 (2025).","journal-title":"Scientific Reports"},{"key":"55088_CR39","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1038\/nmeth.2083","volume":"9","author":"V Ljosa","year":"2012","unstructured":"Ljosa, V., Sokolnicki, K. L. & Carpenter, A. E. Annotated high-throughput microscopy image sets for validation. Nature methods 9, 637 (2012).","journal-title":"Nature methods"},{"key":"55088_CR40","unstructured":"Howard, A. G. Mobilenets: Efficient convolutional neural networks for mobile vision applications (2017) arXiv:1704.04861 arXiv preprint."},{"key":"55088_CR41","doi-asserted-by":"crossref","unstructured":"Wang, Q. Eca-net: Efficient channel attention for deep convolutional neural networks. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition 11534\u201311542 (2020).","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"55088_CR42","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 770\u2013778 (2016).","DOI":"10.1109\/CVPR.2016.90"},{"key":"55088_CR43","unstructured":"Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)."},{"key":"55088_CR44","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L. & Weinberger, K.\u00a0Q. Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4700\u20134708 (2017).","DOI":"10.1109\/CVPR.2017.243"},{"key":"55088_CR45","unstructured":"Tan, M. & Le, Q.\u00a0V. Efficientnet: Rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International Conference on Machine Learning (ICML), 6105\u20136114 (2019)."},{"key":"55088_CR46","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M. & Chu, G. Searching for mobilenetv3. In Proceedings of the IEEE International Conference on Computer Vision (ICCV) 1314\u20131324 (2019).","DOI":"10.1109\/ICCV.2019.00140"},{"key":"55088_CR47","unstructured":"Mehta, S. & Rastegari, M. Separable self-attention for mobile vision transformers. arXiv preprint arXiv:2206.02680 (2022)."},{"key":"55088_CR48","doi-asserted-by":"crossref","unstructured":"Liu, Z. A convnet for the 2020s. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition 11976\u201311986 (2022).","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"55088_CR49","doi-asserted-by":"crossref","unstructured":"Ding, X. Repvgg: Making vgg-style convnets great again. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition 13733\u201313742 (2021).","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"55088_CR50","unstructured":"Gu, A. & Dao, T. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752 (2023)."},{"key":"55088_CR51","unstructured":"Krizhevsky, A., Sutskever, I. & Hinton, G.\u00a0E. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25 (2012)."},{"key":"55088_CR52","unstructured":"Dosovitskiy, A., Beyer, L. & Kolesnikov, A. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations (2021)."},{"key":"55088_CR53","unstructured":"Radford, A., Kim, J. W. & Hallacy, C. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning 8748\u20138763 (2021)."},{"key":"55088_CR54","doi-asserted-by":"crossref","unstructured":"Fang, Y., Wang, W. & Xie, E. Eva-02: A visual representation for neon genesis (2023) arXiv:2303.11331 arXiv preprint.","DOI":"10.2139\/ssrn.4813567"},{"key":"55088_CR55","doi-asserted-by":"crossref","unstructured":"Caron, M., Touvron, H. & Misra, I. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE\/CVF International Conference on Computer Vision 9650\u20139660 (2021).","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"55088_CR56","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E. & Ravi, N. Segment anything. arXiv preprint arXiv:2304.02643 (2023).","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"55088_CR57","doi-asserted-by":"publisher","first-page":"5728","DOI":"10.1038\/s41598-023-31275-7","volume":"13","author":"AK Chanchal","year":"2023","unstructured":"Chanchal, A. K., Lal, S., Kumar, R., Kwak, J. T. & Kini, J. A novel dataset and efficient deep learning framework for automated grading of renal cell carcinoma from kidney histopathology images. Scientific Reports 13, 5728 (2023).","journal-title":"Scientific Reports"},{"key":"55088_CR58","doi-asserted-by":"crossref","unstructured":"Selvaraju, R. R. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision 618\u2013626 (2017).","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-55088-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-55088-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-55088-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T22:32:14Z","timestamp":1786401134000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-55088-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,30]]},"references-count":58,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["55088"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-55088-6","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,30]]},"assertion":[{"value":"20 February 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 May 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","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"24754"}}