{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T06:50:30Z","timestamp":1791269430182,"version":"4.1.0"},"reference-count":46,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T00:00:00Z","timestamp":1791158400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T00:00:00Z","timestamp":1791158400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Small Methods"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Quantitative assessment of the complex morphological and structural changes of cells and organelles during senescence is critical for understanding the level of senescence, which is a key step toward developing anti\u2010aging therapeutics and preventing aging\u2010related diseases. Despite its importance, a robust quantitative method for evaluating cellular senescence is still lacking. Recent advances in holotomography (HT), a label\u2010free imaging technique, address these challenges by enabling high\u2010contrast cell imaging with subcellular specificity, which enables accurate detection and characterization of senescent cells, without the constraints of fluorescence\u2010based imaging. In this study, we introduce an artificial intelligence (AI)\u2010powered Biologically Interpretable Contrastive and Attention\u2010based Network integrated with HT, called BICAN\u2010HT, which leverages high\u2010resolution images to analyze nucleus\u2010cytoplasm interactions for biologically interpretable and quantitative assessment of cellular senescence with enhanced predictive power. We conducted in\u2010depth experiments inducing cellular senescence using three stressors, including hydrogen peroxide (H\n                    <jats:sub>2<\/jats:sub>\n                    O\n                    <jats:sub>2<\/jats:sub>\n                    ),\u00a0doxorubicin (Dox), and palbociclib (Pal), across two different cell lines. BICAN\u2010HT consistently outperformed current state\u2010of\u2010the\u2010art models across multiple stress conditions and cell types, while capturing the morphological features and spatial relationships of cells and organelles within the nucleus and cytoplasm during senescence. BICAN\u2010HT has the potential to transform aging research and accelerate the development of anti\u2010aging interventions.\n                  <\/jats:p>","DOI":"10.1002\/smtd.71091","type":"journal-article","created":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T05:53:28Z","timestamp":1791266008000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of Oxidative Stress\u2010 and Anticancer Drug\u2010Induced Cellular Senescence Using Holotomography and Interpretable Attention\u2010Based Contrastive Learning"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6905-0629","authenticated-orcid":false,"given":"Avinash","family":"Yaganapu","sequence":"first","affiliation":[{"name":"Department of Computer Science University of Nevada Las Vegas  Las Vegas Nevada 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