{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T13:35:37Z","timestamp":1785418537097,"version":"3.56.0"},"reference-count":36,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T00:00:00Z","timestamp":1780358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002923","name":"Consejo Nacional de Investigaciones Cient\u00edficas y T\u00e9cnicas","doi-asserted-by":"publisher","award":["PIP KB1 11220200103286CO"],"award-info":[{"award-number":["PIP KB1 11220200103286CO"]}],"id":[{"id":"10.13039\/501100002923","id-type":"DOI","asserted-by":"publisher"}]},{"award":["PIP KB1 11220200103286CO"],"award-info":[{"award-number":["PIP KB1 11220200103286CO"]}],"id":[{"id":"https:\/\/ror.org\/03cqe8w59","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009483","name":"National Technological University","doi-asserted-by":"publisher","award":["BAICTC546"],"award-info":[{"award-number":["BAICTC546"]}],"id":[{"id":"10.13039\/100009483","id-type":"DOI","asserted-by":"publisher"}]},{"award":["BAICTC546"],"award-info":[{"award-number":["BAICTC546"]}],"id":[{"id":"https:\/\/ror.org\/04t730v47","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Faculty of Engineering, Austral University"},{"id":[{"id":"https:\/\/ror.org\/04043k259","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Connexin-43 (CX43) lateralization in ventricular myocardium has been associated with abnormal impulse propagation and increased arrhythmia susceptibility. Its quantitative assessment in histological sections remains challenging because previous methods require segmentation of individual cardiomyocytes and rely on geometric rules applied to segmented cell profiles. Here, we present CLARISA, a segmentation-free, ROI-based deep learning framework that classifies CX43-positive regions as terminal or lateralized directly from fluorescence images. An expert-annotated dataset was generated from left-ventricular cryosections of Wistar rat hearts, in which CX43-positive regions were labeled according to their distribution pattern. A dual-stream EfficientNetV2-S classifier was trained to capture both local and contextual ROI morphology. We also developed a semi-automated whole-section inference module to generate spatial lateralization probability maps and global percent lateralization estimates. On the held-out test set, CLARISA achieved a ROC-AUC of 0.904 (95% bootstrap CI: 0.828\u20130.960) and a PR-AUC of 0.808 (95% bootstrap CI: 0.682\u20130.913), supporting the feasibility of automated ROI classification for CX43 lateralization assessment. When deployed on whole tissue sections, including an independently analyzed section not used during model development, CLARISA generated spatial maps that captured heterogeneous CX43 organization and produced a global percent lateralization estimate closely aligned with expert annotation, differing by only 1.30 percentage points over the same detected CX43-positive area. Comparison with a previously published segmentation-based method further indicated that ROI-based and cell-segmentation-based approaches provide related but non-equivalent readouts of CX43 lateralization. The ROI-based design additionally reduces annotation burden\u2014requiring classification of discrete CX43-positive signal rather than complex cardiomyocyte delineation\u2014and ensures that all detected CX43-positive signal contributes to the lateralization estimate regardless of cell boundaries. These results establish CLARISA as a proof-of-principle framework for scalable, segmentation-free CX43 lateralization assessment in cardiac tissue. Further validation across larger, independent, and more heterogeneous datasets will be required to assess robustness, portability across imaging conditions, and translational applicability. The complete codebase, pretrained model, image data, and expert annotation tool are publicly available.<\/jats:p>","DOI":"10.3390\/ijms27115033","type":"journal-article","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T14:38:42Z","timestamp":1780411122000},"page":"5033","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CLARISA: Connexin-43 Lateralization Automated ROI-Based Image Signal Analyzer"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-1347-2863","authenticated-orcid":false,"given":"Daniel","family":"Gattari","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Austral University, Pilar B1629WWA, Buenos Aires, Argentina"},{"name":"Functional Genomics and Data Science, Institute for Translational Medicine Research (IIMT), CONICET-Universidad Austral, Pilar B1629AHJ, Buenos Aires, Argentina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9227-5446","authenticated-orcid":false,"given":"Joseba","family":"Sancho-Zamora","sequence":"additional","affiliation":[{"name":"Tecnun School of Engineering, Universidad de Navarra, 31009 Donostia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0125-7345","authenticated-orcid":false,"given":"Debora","family":"Chan","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Austral University, Pilar B1629WWA, Buenos Aires, Argentina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7280-2123","authenticated-orcid":false,"given":"Natalia Jorgelina","family":"Prado","sequence":"additional","affiliation":[{"name":"Institute of Experimental Medicine and Biology of Cuyo (IMBECU), CONICET, Facultad de Ciencias M\u00e9dicas, Universidad Nacional de Cuyo, Mendoza 5500, Argentina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5163-3703","authenticated-orcid":false,"given":"Emiliano Ra\u00fal","family":"Diez","sequence":"additional","affiliation":[{"name":"Institute of Experimental Medicine and Biology of Cuyo (IMBECU), CONICET, Facultad de Ciencias M\u00e9dicas, Universidad Nacional de Cuyo, Mendoza 5500, Argentina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8181-7236","authenticated-orcid":false,"given":"Mariano Llamedo","family":"Soria","sequence":"additional","affiliation":[{"name":"Electronics Department, National Technological University, Buenos Aires C1041AAJ, Argentina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario","family":"Rossi","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Austral University, Pilar B1629WWA, Buenos Aires, Argentina"},{"name":"Functional Genomics and Data Science, Institute for Translational Medicine Research (IIMT), CONICET-Universidad Austral, Pilar B1629AHJ, Buenos Aires, Argentina"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1161\/01.RES.76.3.366","article-title":"The stochastic nature of cardiac propagation at a microscopic level. 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