{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T08:03:14Z","timestamp":1785225794202,"version":"3.55.0"},"reference-count":0,"publisher":"The Nencki Institute of Experimental Biology, Polish Academy of Sciences","license":[{"start":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:00:00Z","timestamp":1785196800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Acta Neurobiol. Exp."],"abstract":"<jats:p>Proliferating cell nuclear antigen (PCNA) forms subnuclear replication patterns that report S\u2011phase progression with a single marker and provide an imaging readout of the replication factor C (RFC)\u2011PCNA axis. We benchmarked lightweight convolutional neural networks for classifying fixed HeLa cell PCNA images as G1, early S, mid S, or late S, and then applied the best\u2011performing model to live\u2011cell RFC control and RFC\u2011del datasets. MobileNetV2 provided the best balance of accuracy and inference cost in the fixed\u2011cell benchmark. In live\u2011cell traces, RFC\u2011del cells showed prolonged late S\u2011like PCNA pattern duration, whereas mid S\u2011like duration and peak\u2011distance metrics did not show clear group separation. These findings support lightweight PCNA\u2011pattern classification as a practical tool for quantitative replication\u2011pattern phenotyping and indicate a possible future application to neurobiological imaging, such as automated quantification of pro liferating cells in neurogenesis studies.<\/jats:p>","DOI":"10.55782\/2hr5ac70","type":"journal-article","created":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T07:53:07Z","timestamp":1785225187000},"source":"Crossref","is-referenced-by-count":0,"title":["Lightweight CNN recognition of HeLa cell PCNA\u2011based S\u2011phase subphases and RFC\u2011dependent dynamics"],"prefix":"10.55782","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8732-5822","authenticated-orcid":true,"given":"Maciej","family":"Krupa","sequence":"first","affiliation":[{"name":"Laboratory of Imaging of Tissue Structure and Function, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J\u0119drzej","family":"Szyma\u0144ski","sequence":"additional","affiliation":[{"name":"Laboratory of Imaging of Tissue Structure and Function, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"34635","published-online":{"date-parts":[[2026,7,28]]},"container-title":["Acta Neurobiologiae Experimentalis"],"original-title":[],"link":[{"URL":"https:\/\/ane.pl\/index.php\/ane\/article\/download\/2818\/2575","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ane.pl\/index.php\/ane\/article\/download\/2818\/2575","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T07:53:07Z","timestamp":1785225187000},"score":1,"resource":{"primary":{"URL":"https:\/\/ane.pl\/index.php\/ane\/article\/view\/2818"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,28]]},"references-count":0,"URL":"https:\/\/doi.org\/10.55782\/2hr5ac70","relation":{},"ISSN":["1689-0035","0065-1400"],"issn-type":[{"value":"1689-0035","type":"electronic"},{"value":"0065-1400","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7,28]]}}}