{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T12:32:56Z","timestamp":1777984376623,"version":"3.51.4"},"update-to":[{"DOI":"10.12688\/f1000research.171338.1","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T00:00:00Z","timestamp":1769817600000}}],"reference-count":8,"publisher":"F1000 Research Ltd","license":[{"start":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T00:00:00Z","timestamp":1769817600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["f1000research.com"],"crossmark-restriction":false},"short-container-title":["F1000Res"],"abstract":"<ns3:p>\n                    Background Myocardial ischemia is a dynamic, complex process characterized by hyperkalemia, acidosis, and ATP depletion. While these three conditions alter cardiomyocyte electrophysiology, it is difficult to discern how much each one individually contributes to the resulting changes in action potential (AP). In this study, we test whether machine learning can deconvolute these distinct ischemic patterns within a single AP. Methods We developed a multi-target regression model trained on data generated by the Luo-Rudy (1991) computational model of a ventricular cardiomyocyte, simulating a wide range of ischemic conditions. The model was designed to predict two continuous variables: extracellular potassium concentration ([K\n                    <ns3:sup>+<\/ns3:sup>\n                    ]o) and intracellular pH (pHi). Results The model achieved high accuracy on a held-out test set, with mean squared errors (MSE) below 0.25 for [K\n                    <ns3:sup>+<\/ns3:sup>\n                    ]o and below 0.01 for pHi. To further generalize this model, we applied this trained model to a structurally distinct model, the Ten Tusscher (2006) framework. We were able to accurately predict [K\n                    <ns3:sup>+<\/ns3:sup>\n                    ]o and pHi from APs, demonstrating that the learned principles are robust. A feature importance analysis revealed that resting membrane potential (RMP) was the strongest predictor for [K\n                    <ns3:sup>+<\/ns3:sup>\n                    ]o, while action potential duration (APD) is most important for predicting pHi, underscoring these distinct cardiomyocyte electrophysiological patterns Conclusions Our approach can distinguish distinct ischemic drivers and has potential for\n                    <ns3:italic>in silico<\/ns3:italic>\n                    drug screening and mechanistic analysis.\n                  <\/ns3:p>","DOI":"10.12688\/f1000research.171338.2","type":"journal-article","created":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T07:04:06Z","timestamp":1769843046000},"page":"1341","update-policy":"https:\/\/doi.org\/10.12688\/f1000research.crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Deconvoluting the Electrophysiological Signatures of Myocardial Ischemia using a Validated Machine Learning Framework"],"prefix":"10.12688","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9107-3704","authenticated-orcid":false,"given":"Ahmad","family":"Mahmood","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0435-7324","authenticated-orcid":false,"given":"Kiel","family":"Jacqueline","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3533-434X","authenticated-orcid":false,"given":"Joanne","family":"Lac","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2560","published-online":{"date-parts":[[2026,1,31]]},"reference":[{"key":"ref2","doi-asserted-by":"publisher","first-page":"917-1017","DOI":"10.1152\/physrev.1999.79.3.917","article-title":"Cardiac ionic currents and acute ischemia: from channels to arrhythmias.","volume":"79","author":"E Carmeliet","year":"1999","journal-title":"Physiol. Rev."},{"key":"ref4","first-page":"557-567","article-title":"The KATP channel in ischaemia and arrhythmogenesis.","volume":"32","author":"A Wilde","year":"2000","journal-title":"Ann. Med."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"101482","DOI":"10.1016\/j.cpcardiol.2022.101482","article-title":"Machine Learning Approach on High Risk Treadmill Exercise Test to Predict Obstructive Coronary Artery Disease by using P, QRS, and T waves\u2019 Features.","volume":"48","author":"A Yilmaz","year":"2023","journal-title":"Curr. Probl. Cardiol."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"6","DOI":"10.3390\/jcdd12010006","article-title":"A New Risk Prediction Model for the Assessment of Myocardial Injury in Elderly Patients Undergoing Non-Elective Surgery.","volume":"12","author":"V Cicek","year":"2024","journal-title":"J. Cardiovasc. Dev. Dis."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"1501-1526","DOI":"10.1161\/01.RES.68.6.1501","article-title":"A model of the ventricular cardiac action potential. Depolarization, repolarization, and their interaction.","volume":"68","author":"C Luo","year":"1991","journal-title":"Circ. Res."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"H1088-H1100","DOI":"10.1152\/ajpheart.00109.2006","article-title":"A model for human ventricular tissue.","volume":"291","author":"K Ten Tusscher","year":"2006","journal-title":"Am. J. Phys. Heart Circ. Phys."},{"key":"ref7","first-page":"63-74","article-title":"Acidosis and cardiac contractility.","volume":"153","author":"C Orchard","year":"2021","journal-title":"J. Mol. Cell. Cardiol."},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.17216134","article-title":"Deconvoluting the Electrophysiological Signatures of Myocardial Ischemia using a Validated Machine Learning Framework (Version 1.0) [Computer software].","author":"M Ahmad","year":"2025","journal-title":"Zenodo."}],"container-title":["F1000Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/f1000research.com\/articles\/14-1341\/v2\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/f1000research.com\/articles\/14-1341\/v2\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/f1000research.com\/articles\/14-1341\/v2\/iparadigms","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T03:19:10Z","timestamp":1770693550000},"score":1,"resource":{"primary":{"URL":"https:\/\/f1000research.com\/articles\/14-1341\/v2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,31]]},"references-count":8,"URL":"https:\/\/doi.org\/10.12688\/f1000research.171338.2","relation":{"has-version":[{"id-type":"doi","id":"10.12688\/f1000research.171338.1","asserted-by":"subject"}],"has-review":[{"id-type":"doi","id":"10.5256\/f1000research.195736.r454211","asserted-by":"subject"},{"id-type":"doi","id":"10.5256\/f1000research.195736.r454212","asserted-by":"subject"},{"id-type":"doi","id":"10.5256\/f1000research.195736.r454211","asserted-by":"object"},{"id-type":"doi","id":"10.5256\/f1000research.195736.r454212","asserted-by":"object"}]},"ISSN":["2046-1402"],"issn-type":[{"value":"2046-1402","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,31]]},"assertion":[{"value":"Indexed","URL":"https:\/\/f1000research.com\/articles\/14-1341\/v2#article-reports","order":0,"name":"referee-status","label":"Referee status","group":{"name":"current-referee-status","label":"Current Referee Status"}},{"value":"10.5256\/f1000research.195736.r454212, Mert \u0130lker Hay\u0131ro\u011flu, Dr. Siyami Ersek Thoracic and Cardiovascular Surgery Training and Research Hospital, Istanbul, 02 Feb 2026, version 2, indexed","URL":"https:\/\/f1000research.com\/articles\/14-1341\/v2#referee-response-454212","order":0,"name":"referee-response-454212","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.5256\/f1000research.195736.r454211, Cenitta D, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 10 Feb 2026, version 2, indexed","URL":"https:\/\/f1000research.com\/articles\/14-1341\/v2#referee-response-454211","order":1,"name":"referee-response-454211","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"The author(s) declared that no grants were involved in supporting this work","order":2,"name":"grant-information","label":"Grant Information"},{"value":"This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","order":0,"name":"copyright-info","label":"Copyright"}]}}