Investigations of the Navß1b sodium channel subunit in human ventricle; functional characterization of the H162P Brugada syndrome mutant.

Brugada syndrome (BrS) is a rare inherited disease that can give rise to ventricular arrhythmia and ultimately sudden cardiac death. Numerous loss-of-function mutations in the cardiac sodium channel Nav1.5 have been associated with BrS. However, few mutations in the auxiliary Navß1-4 subunits have been linked to this disease. Here we investigated differences in expression and function between Navß1 and Navß1b and whether the H162P/Navß1b mutation found in a BrS patient is likely to be the underlying cause of disease. The impact of Navß subunits was investigated by patch-clamp electrophysiology, and the obtained in vitro values were used for subsequent in silico modeling. We found that Navß1b transcripts were expressed at higher levels than Navß1 transcripts in the human heart. Navß1 and Navß1b coexpressed with Nav1.5 induced a negative shift on steady state of activation and inactivation compared with Nav1.5 alone. Furthermore, Navß1b was found to increase the current level when coexpressed with Nav1.5, Navß1b/H162P mutated subunit peak current density was reduced by 48% (-645 ± 151 vs. -334 ± 71 pA/pF), V1/2 steady-state inactivation shifted by -6.7 mV (-70.3 ± 1.5 vs. -77.0 ± 2.8 mV), and time-dependent recovery from inactivation slowed by >50% compared with coexpression with Navß1b wild type. Computer simulations revealed that these electrophysiological changes resulted in a reduction in both action potential amplitude and maximum upstroke velocity. The experimental data thereby indicate that Navß1b/H162P results in reduced sodium channel activity functionally affecting the ventricular action potential. This result is an important replication to support the notion that BrS can be linked to the function of Navß1b and is associated with loss-of-function of the cardiac sodium channel.

Artificial intelligence for the detection, prediction, and management of atrial fibrillation.

The present article reviews the state of the art of machine learning algorithms for the detection, prediction, and management of atrial fibrillation (AF), as well as of the development and evaluation of artificial intelligence (AI) in cardiology and beyond. Today, AI detects AF with a high accuracy using 12-lead or single-lead electrocardiograms or photoplethysmography. The prediction of paroxysmal or future AF currently operates at a level of precision that is too low for clinical use. Further studies are needed to determine whether patient selection for interventions may be possible with machine learning.

Importance of material parameters and strain energy function on the wall stresses in the left ventricle.

Patient-specific estimates of the stress distribution in the left ventricles (LV) may have important applications for therapy planning, but computing the stress generally requires knowledge of the material behaviour. The passive stress-strain relation of myocardial tissue has been characterized by a number of models, but material parameters (MPs) remain difficult to estimate. The aim of this study is to implement a zero-pressure algorithm to reconstruct numerically the stress distribution in the LV without precise knowledge of MPs. We investigate the sensitivity of the stress distribution to variations in the different sets of constitutive parameters. We show that the sensitivity of the LV stresses to MPs can be marginal for an isotropic constitutive model. However, when using a transversely isotropic exponential strain energy function, the LV stresses become sensitive to MPs, especially to the linear elastic coefficient before the exponential function. This indicates that in-vivo identification efforts should focus mostly on this MP for the development of patient-specific finite-element analysis.