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A Feature Selection-Incorporated Simulation Study to Reveal the Effect of Calcium Ions on Cardiac Repolarization Alternans during Myocardial Ischemia.

Authors :
Gu, Kaihao
Geng, Zihui
Yang, Yuwei
Yan, Shengjie
Hu, Bo
Wu, Xiaomei
Source :
Applied Sciences (2076-3417); Aug2024, Vol. 14 Issue 15, p6789, 15p
Publication Year :
2024

Abstract

(1) Background: The main factors and their interrelationships contributing to cardiac repolarization alternans (CRA) remain unclear. This study aimed to elucidate the calcium (Ca<superscript>2+</superscript>)-related mechanisms underlying myocardial ischemia (MI)-induced CRA. (2) Materials and Methods: CRA was induced using S1 stimuli for pacing in an in silico ventricular model with MI. The standard deviations of nine Ca<superscript>2+</superscript>-related subcellular parameters among heartbeats from 100 respective nodes with and without alternans were chosen as features, including the maximum systole and end-diastole and corresponding differences in the Ca<superscript>2+</superscript> concentration in the intracellular region([Ca<superscript>2+</superscript>]<subscript>i</subscript>) and junctional sarcoplasmic reticulum ([Ca<superscript>2+</superscript>]<subscript>jsr</subscript>), as well as the maximum opening of the L-type Ca<superscript>2+</superscript> current (I<subscript>CaL</subscript>) voltage-dependent activation gate (d-gate), maximum closing of the inactivation gate (ff-gate), and the gated channel opening time (GCOT). Feature selection was applied to determine the importance of these features. (3) Results: The major parameters affecting CRA were the differences in [Ca<superscript>2+</superscript>]<subscript>i</subscript> at end-diastole, followed by the extent of d-gate activation and GCOT among beats. (4) Conclusions: MI-induced CRA is primarily characterized by functional changes in Ca<superscript>2+</superscript> re-uptake, leading to alternans of [Ca<superscript>2+</superscript>]<subscript>i</subscript> and subsequent alternans of I<subscript>CaL</subscript>-dependent properties. The combination of computational simulation and machine learning shows promise in researching the underlying mechanisms of cardiac electrophysiology. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20763417
Volume :
14
Issue :
15
Database :
Complementary Index
Journal :
Applied Sciences (2076-3417)
Publication Type :
Academic Journal
Accession number :
178949762
Full Text :
https://doi.org/10.3390/app14156789