Unlocking the potential of artificial intelligence in electrocardiogram biometrics: age-related changes, anomaly detection, and data authenticity in mobile health platforms.

Aims: Mobile devices such as smartphones and watches can now record single-lead electrocardiograms (ECGs), making wearables a potential screening tool for cardiac and wellness monitoring outside of healthcare settings. Because friends and family often share their smart phones and devices, confirmation that a sample is from a given patient is important before it is added to the electronic health record.

Methods And Results: We sought to determine whether the application of Siamese neural network would permit the diagnostic ECG sample to serve as both a medical test and biometric identifier.

Creating Ion Channel Kinetic Models Using Cloud Computing.

Computational modeling of ion channels provides key insight into experimental electrophysiology results and can be used to connect channel dynamics to emergent phenomena observed at the tissue and organ levels. However, creation of these models requires substantial mathematical and computational background. This tutorial seeks to lower the barrier to creating these models by providing an automated pipeline for creating Markov models of an ion channel kinetics dataset.

Identification of structures for ion channel kinetic models.

Markov models of ion channel dynamics have evolved as experimental advances have improved our understanding of channel function. Past studies have examined limited sets of various topologies for Markov models of channel dynamics. We present a systematic method for identification of all possible Markov model topologies using experimental data for two types of native voltage-gated ion channel currents: mouse atrial sodium currents and human left ventricular fast transient outward potassium currents.

A Molecularly Detailed Na1.5 Model Reveals a New Class I Antiarrhythmic Target.

Antiarrhythmic treatment strategies remain suboptimal due to our inability to predict how drug interactions with ion channels will affect the ability of the tissues to initiate and sustain an arrhythmia. We built a multiscale molecular model of the Na channel domain III (domain III voltage-sensing domain) to highlight the molecular underpinnings responsible for mexiletine drug efficacy. This model predicts that a hyperpolarizing shift in the domain III voltage-sensing domain is critical for drug efficacy and may be leveraged to design more potent Class I molecules.

Mechanisms and models of cardiac sodium channel inactivation.

Shortly after cardiac Na channels activate and initiate the action potential, inactivation ensues within milliseconds, attenuating the peak Na current, I and allowing the cell membrane to repolarize. A very limited number of Na channels that do not inactivate carry a persistent I, or late I. While late I is only a small fraction of peak magnitude, it significantly prolongs ventricular action potential duration, which predisposes patients to arrhythmia.

Quantifying functional group interactions that determine urea effects on nucleic acid helix formation.

Urea destabilizes helical and folded conformations of nucleic acids and proteins, as well as protein-nucleic acid complexes. To understand these effects, extend previous characterizations of interactions of urea with protein functional groups, and thereby develop urea as a probe of conformational changes in protein and nucleic acid processes, we obtain chemical potential derivatives (μ23 = dμ2/dm3) quantifying interactions of urea (component 3) with nucleic acid bases, base analogues, nucleosides, and nucleotide monophosphates (component 2) using osmometry and hexanol-water distribution assays. Dissection of these μ23 values yields interaction potentials quantifying interactions of urea with unit surface areas of nucleic acid functional groups (heterocyclic aromatic ring, ring methyl, carbonyl and phosphate O, amino N, sugar (C and O); urea interacts favorably with all these groups, relative to interactions with water.