ArticleJournal of the American Heart Association2026
Development of Electrocardiography Standards for Evaluating Myocardial Infarction and Ischemia-Reperfusion Injury in Mice.
Article in Journal of the American Heart Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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13 authors.
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Abstract
backgroundAcute and chronic heart failure secondary to myocardial infarction (MI) and cardiac ischemia-reperfusion injury (IRI) are leading causes of death in ischemic heart disease. A mouse model is indispensable for investigating MI and IRI, and the development of reliable mouse MI and IRI models is essential for advancing research in this field. The clear early diagnostic criteria for confirming successful induction of MI and IRI in mice remain lacking.
methodsAdult C57BL/6J background mice underwent left anterior descending coronary artery ligation to induce acute MI, or ligation followed by reperfusion to induce IRI. The success of the MI and IRI model establishment was confirmed by 2,3,5-triphenyltetrazolium chloride staining and echocardiography. Electrocardiography was used to monitor the electric activity in the mice.
conclusionsElectrocardiography demonstrated that ST-segment elevation in ECG lead II and corrected QTc interval prolongation at 30 minutes following left anterior descending ligation as 2 key early indicators of successful MI. Echocardiography analysis revealed that the magnitude of ST-segment elevation strongly correlated with the left anterior descending ligation site, where a more proximal ligation produced a greater ST-segment elevation amplitude and more severe ischemia. In IRI models, ST-segment elevation typically resolved and returned to baseline within 20 minutes of reperfusion. This study developed quantifiable early diagnostic criteria for successful MI and IRI induction based on characteristic ECG changes. These quantifiable ECG parameters provide early diagnostic standards that can significantly streamline and optimize modeling procedures.
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