Recent reports indicate dominant roles of TRAAK and TREK-1 channels, i.e., mechanosensitive two-pore-domain potassium channels (K2P) at the nodes of Ranvier for action potential repolarization in mammalian peripheral nerves. Functional changes in mammalian peripheral nerve conduction by mechanical stretch studied by recording compound action potentials lack the necessary resolution to detect subtle neuromodulatory effects on conduction velocity. In this study, we developed a novel in vitro approach that enables single-fiber recordings from individual mouse sciatic nerve axons while delivering computer-controlled stepped stretch to the sciatic nerve trunk. Axial stretch instantaneously increased the conduction delay in both myelinated A-fibers and unmyelinated C-fibers. Increases in conduction delay linearly correlated with increases in axial stretch ratio for both A-and C-fibers. The slope of the increase in conduction delay versus stretch ratio was steeper in C-fibers than in A-fibers. Moderate axial stretch (14−19% of in vitro length) reversibly blocked 37.5% of unmyelinated C-fibers but none of the eight myelinated A-fibers tested. Application of arachidonic acid, an agonist to TRAAK and TREK-1 to sciatic nerve trunk, blocks axonal transmission in both A-and C-fibers with delayed onset and prolonged block. Also, the application of an antagonist ruthenium red showed a tendency of suppressing the stretch-evoked increase in conduction delay. These results could draw focused research on pharmacological and mechanical activation of K2P channels as a novel neuromodulatory strategy to achieve peripheral nerve block.

There is growing interest to better understand drug-induced cardiovascular complications and to predict undesirable side effects at as early a stage in the drug development process as possible. The purpose of this paper is to investigate computationally the influence of sodium ion channel blockage on cardiac electromechanics. To do so, we implement a myofiber orientation dependent passive stress model (Holzapfel-Ogden) in the multiphysics solver Chaste to simulate an imaged physiological model of the human ventricles. A dosage of a sodium channel blocker was then applied and its inhibitory effects on the electrical propagation across ventricles were modeled. We employ the Kerckhoffs active stress model to generate electrically excited contractile behavior of myofibers. Our predictions indicate that a delay in the electrical activation of ventricular tissue caused by the sodium channel blockage translates to a delay in the mechanical biomarkers that were investigated. Moreover, sodium channel blockage was found to increase left ventricular twist. A multiphysics computational framework from the cell level to the organ level was thus used to predict the effect of sodium channel blocking drugs on cardiac electromechanics.

Abstract In this manuscript we propose a method to generate Purkinje networks that are anatomically and physiologically plausible, for use with in-silico modeling. Purkinje networks play a fundamental role in shaping cardiac electrical activation patterns and their corresponding clinical electrocardiograms (ECGs). Despite a known variability in ventricular activation sequences, certain sites of early activation within the left and right ventricles have been identified in the literature for normal electrical excitation patterns. Nevertheless, in-vivo imaging of Purkinje networks cannot at present yield detailed information on their structure, so there is a genuine need for in-silico models that can construct Purkinje networks that are both anatomically and physiologically plausible, in particular networks that can exhibit correctly situated early activation sites in the ventricles. Of special interest to this manuscript is the method of representation of Purkinje networks by line-like electrical elements that are generated by means of a fractal-tree algorithm (1–3) to overlay the irregular endocardial surfaces. A known drawback to a direct implementation of this approach in complex geometries relates to its incorrect modeling of clinically observed ECGs and electrical activation sequences for a human heart (4). Our aim was thus to correct this deficiency by generating Purkinje networks that leverage a pre-knowledge of the location of early activation sites. At every such site we first generate a Purkinje sub-network. These sub-networks are linked together and to the bundle of His, setting up our first stage of the Purkinje network. Subsequently, we spawn a second stage to the Purkinje network from one or more tips of any given sub-network, to cover the full endocardial surface with Purkinje elements. Our resulting activation sequences and ECGs compare favorably to those of a population of 39 healthy male individuals (the PTB diagnostic database), and our corresponding mechanical markers of cardiac function also match well with the literature.