{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T20:46:13Z","timestamp":1782938773236,"version":"3.54.5"},"reference-count":41,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"vor","delay-in-days":14,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS\u20102021\u2010NR060106"],"award-info":[{"award-number":["RS\u20102021\u2010NR060106"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Aging Cell"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Aging reshapes the architecture of human skeletal muscle, yet objective tissue\u2010level markers that capture this process remain limited. We combined large\u2010scale histology with deep learning to identify reproducible features of muscle aging and to test their biological relevance. We analyzed 974 hematoxylin\u2013eosin whole\u2010slide images from a population resource using a dual\u2010attention convolutional neural network and an independent Mask R\u2010CNN model to quantify nuclear size and density, verified by manual review. The classifier distinguished young from aged muscle with high accuracy (AUC 0.91; accuracy 86.2%), and attention maps consistently highlighted nuclear enlargement and spatial disorganization as salient features. Nuclear diameter increased with age (Spearman's\n                    <jats:italic>\u03c1<\/jats:italic>\n                    \u2009=\u20090.71,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.0001) across automated and manual measurements. Transcriptomes matched to the same donors showed that samples with larger nuclei were enriched for pathways related to chromatin remodeling, proteostasis, cellular senescence, mitochondrial activity, and telomere regulation, whereas smaller nuclei aligned with anti\u2010inflammatory and DNA repair programs. External pediatric inflammatory myopathies exhibited nuclear enlargement comparable to aged muscle, suggesting inflammation\u2010related premature histologic aging. These findings identify nuclear enlargement as a robust, quantifiable feature that integrates structural and molecular signatures of muscle aging. The proposed deep learning\u2013based nuclear morphometry provides a scalable framework for tissue\u2010level aging biomarkers and suggests a potential \u201cmuscle aging clock\u201d applicable to both physiological aging and disease states.\n                  <\/jats:p>","DOI":"10.1111\/acel.70577","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T13:15:03Z","timestamp":1781529303000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Nuclear Enlargement as a Histological Hallmark of Skeletal Muscle Aging, Revealed by Deep Learning\u2010Driven Analysis and Validated in Inflammatory Myopathies"],"prefix":"10.1111","volume":"25","author":[{"given":"Tam","family":"Dao","sequence":"first","affiliation":[{"name":"Department of Biomedical Science and Engineering Gwangju Institute of Science and Technology (GIST)  Gwangju Republic of Korea"},{"name":"Department of Physiology Sungkyunkwan University School of Medicine  Suwon Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thanh T.","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Department of Biomedical Science and Engineering Gwangju Institute of Science and Technology (GIST)  Gwangju Republic of Korea"},{"name":"Center of Endocrinology, Metabolism, Genetic\/Genomics and Molecular Therapy Vietnam National Children's Hospital  Hanoi Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gia Minh","family":"Hoang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Science and Engineering Gwangju Institute of Science and Technology (GIST)  Gwangju Republic of Korea"},{"name":"Department of Physiology and Biomedical Engineering Mayo Clinic  Scottsdale Arizona USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3779-0645","authenticated-orcid":false,"given":"Junhyeon","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Biomedical Science and Engineering Gwangju Institute of Science and Technology (GIST)  Gwangju Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunju","family":"Jo","sequence":"additional","affiliation":[{"name":"Department of Biomedical Science and Engineering Gwangju Institute of Science and Technology (GIST)  Gwangju Republic of Korea"},{"name":"Department of Microbiology Wonkwang University School of Medicine  Iksan Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thach Hoang","family":"Ngoc","sequence":"additional","affiliation":[{"name":"Department of Pathology Vietnam National Children's Hospital  Ha Noi Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diep Hong","family":"Pho","sequence":"additional","affiliation":[{"name":"Department of Pathology Vietnam National Children's Hospital  Ha Noi Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dien Tran","family":"Minh","sequence":"additional","affiliation":[{"name":"Center of Endocrinology, Metabolism, Genetic\/Genomics and Molecular Therapy Vietnam National Children's Hospital  Hanoi Vietnam"},{"name":"Department of Pathology Vietnam National Children's Hospital  Ha Noi Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emma Anh","family":"Ton","sequence":"additional","affiliation":[{"name":"Department of Computer Science Harvard John A. Paulson School of Engineering and Applied Sciences  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sunjae","family":"Lee","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering Biology Korea Advanced Institute of Science &amp; Technology (KAIST)  Daejeon Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyun Jin","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Physiology Sungkyunkwan University School of Medicine  Suwon Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vu Chi","family":"Dung","sequence":"additional","affiliation":[{"name":"Center of Endocrinology, Metabolism, Genetic\/Genomics and Molecular Therapy Vietnam National Children's Hospital  Hanoi Vietnam"},{"name":"Department of Pediatrics University of Medicine and Pharmacy\u2014Vietnam National University  Ha Noi Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1010-7712","authenticated-orcid":false,"given":"Jae Gwan","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Biomedical Science and Engineering Gwangju Institute of Science and Technology (GIST)  Gwangju Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongryeol","family":"Ryu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Science and Engineering Gwangju Institute of Science and Technology (GIST)  Gwangju Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,15]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1242\/jcs.02602"},{"key":"e_1_2_11_3_1","unstructured":"Chakradeo K. P.Nielsen L. M. R.Gjerdrum et\u00a0al.2024. \u201cContrastive Deep Learning Reveals Age Biomarkers in Histopathological Skin Biopsies.\u201d ArXiv preprint arXiv:2411.16956."},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijms25031833"},{"key":"e_1_2_11_5_1","volume-title":"Hands\u2010On Computer Vision With Detectron2: Develop Object Detection and Segmentation Models With a Code and Visualization Approach","author":"Dang T.","year":"2023"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.3803\/EnM.2020.405"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.3969"},{"key":"e_1_2_11_8_1","volume-title":"Myogenic Precursor Cell Impairment in Dermatomyositis and Other Myositis","author":"Gallay L.","year":"2020"},{"key":"e_1_2_11_9_1","unstructured":"Gildenblat J.2024. \u201cContributors: Pytorch Library for Cam Methods (2021).\u201dhttps:\/\/github.com\/jacobgil\/pytorch\u2010grad\u2010cam."},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.4103\/2153\u20103539.119005"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1042\/CS20230319"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.2653"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijms25052742"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tcb.2018.02.001"},{"issue":"6","key":"e_1_2_11_16_1","first-page":"524","article-title":"Telomere Dysfunction, Autoimmunity and Aging","volume":"2","author":"Hohensinner P. 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S.Gross S.Chintala et\u00a0al.2017.\u201cAutomatic Differentiation in Pytorch. Computer Vision \u2013 ECCV 2018. ECCV 2018. 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