A deep learning network can classify cardiac myocytes into drug-free and drugged categories and predict the impact of electrophysiological perturbation across the continuum of aging.

A novel computational approach integrates personal genetics into personalized whole heart digital twins to predict ventricular arrhythmia circuits for patients with arrhythmogenic right ventricular cardiomyopathy.

Feedback sensing of the intracellular calcium concentration suffices to reproduce the diversity of ionic conductances underlying normal cardiac electromechanical function in a genetically diverse population of mice.

Personalized virtual-heart technology for arrhythmia risk assessment could transform the management of hypertrophic cardiomyopathy patients, eliminating many unnecessary primary-prevention defibrillator deployments while ensuring patients at high risk for arrhythmia are adequately protected.

Cardiac-specific neurons residing in stellate ganglia comprised of three subsets of neurons based upon their transcriptomic and neurochemical properties and modulates cardiac sympathetic control.

Vigorous endurance training results in prolongation of cardiac repolarization and increased repolarization instability, mild ventricular fibrosis associated with decrease of the transient outward potassium current, and enhanced arrhythmia susceptibility in canine model.

A computer model of human cardiomyocyte was produced and validated on independent datasets, overcoming shortcomings of its predecessors, also yielding broadly relevant insights and results on major ionic currents.

Neural networks can emulate normal and abnormal human ventricular action potentials accurately and potentially much faster than regular simulations paving the way for more efficient quantitative systems pharmacology studies.

In models reconstructed from MRI scans of patients with embolic stroke of undetermined source, computational simulations reveal that fibrosis has the intrinsic capacity to sustain arrhythmia drivers when subjected to triggered activity known to exist in patients with atrial fibrillation.

Ex vivo, in vitro, and computational modeling studies indicate a persistence of myofibroblast senescence in the infarct border zone with age which can disrupt conduction and promote arrhythmias via senescent myofibroblast-cardiomyocyte coupling.