{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T20:05:58Z","timestamp":1786133158282,"version":"build-2736575974"},"reference-count":31,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"11","funder":[{"DOI":"10.13039\/100000062","name":"NIDDK","doi-asserted-by":"publisher","award":["R01 DK114485"],"award-info":[{"award-number":["R01 DK114485"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Institutes of Health OD","award":["R01 DK114485 02S1"],"award-info":[{"award-number":["R01 DK114485 02S1"]}]},{"name":"National Institutes of Health OD","award":["R01 DK114485 03S1"],"award-info":[{"award-number":["R01 DK114485 03S1"]}]},{"name":"National Institutes of Health OD","award":["U01 DK103225"],"award-info":[{"award-number":["U01 DK103225"]}]},{"name":"National Institutes of Health OD","award":["U2C DK114886"],"award-info":[{"award-number":["U2C DK114886"]}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["BE-3801"],"award-info":[{"award-number":["BE-3801"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":["JASN"],"published-print":{"date-parts":[[2021,11]]},"abstract":"<jats:sec>\n                    <jats:title>Significance Statement<\/jats:title>\n                    <jats:p>Podocytes are depleted in several renal parenchymal processes. The current gold standard to identify podocytes considers histopathologic staining of nuclei using specific antibodies and manual enumeration, which is expensive and laborious. We have developed PodoSighter, a cloud-based tool for automated, label-free podocyte detection, and three-dimensional quantification from periodic acid\u2013Schiff-stained histologic sections. A diverse dataset from rodent models of glomerular diseases (diabetic kidney disease, crescentic GN, and dose-dependent direct podocyte toxicity and depletion), human biopsies for steroid resistant nephrotic syndrome, and human autopsy tissue, demonstrate generalizability of the tool. Samples were derived from multiple laboratory, supporting broad application. This tool may facilitate clinical assessment and research involving podocyte morphometry.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Podocyte depletion precedes progressive glomerular damage in several kidney diseases. However, the current standard of visual detection and quantification of podocyte nuclei from brightfield microscopy images is laborious and imprecise.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We have developed PodoSighter, an online cloud-based tool, to automatically identify and quantify podocyte nuclei from giga-pixel brightfield whole-slide images (WSIs) using deep learning. Ground-truth to train the tool used immunohistochemically or immunofluorescence-labeled images from a multi-institutional cohort of 122 histologic sections from mouse, rat, and human kidneys. To demonstrate the generalizability of our tool in investigating podocyte loss in clinically relevant samples, we tested it in rodent models of glomerular diseases, including diabetic kidney disease, crescentic GN, and dose-dependent direct podocyte toxicity and depletion, and in human biopsies from steroid-resistant nephrotic syndrome and from human autopsy tissues.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The optimal model yielded high sensitivity\/specificity of 0.80\/0.80, 0.81\/0.86, and 0.80\/0.91, in mouse, rat, and human images, respectively, from periodic acid\u2013Schiff-stained WSIs. Furthermore, the podocyte nuclear morphometrics extracted using PodoSighter were informative in identifying diseased glomeruli. We have made PodoSighter freely available to the general public as turnkey plugins in a cloud-based web application for end users.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our study demonstrates an automated computational approach to detect and quantify podocyte nuclei in standard histologically stained WSIs, facilitating podocyte research, and enabling possible future clinical applications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1681\/asn.2021050630","type":"journal-article","created":{"date-parts":[[2021,9,3]],"date-time":"2021-09-03T14:10:40Z","timestamp":1630678240000},"page":"2795-2813","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":22,"title":["PodoSighter: A Cloud-Based Tool for Label-Free Podocyte Detection in Kidney Whole-Slide 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