Reduced cardiac contractility during heart failure (HF) is linked to impaired Ca2+ release from Ryanodine Receptors (RyRs). We investigated whether this deficit can be traced to nanoscale RyR reorganization. Using super-resolution imaging, we observed dispersion of RyR clusters in cardiomyocytes from post-infarction HF rats, resulting in more numerous, smaller clusters. Functional groupings of RyR clusters which produce Ca2+ sparks (Ca2+ release units, CRUs) also became less solid. An increased fraction of small CRUs in HF was linked to augmented ‘silent’ Ca2+ leak, not visible as sparks. Larger multi-cluster CRUs common in HF also exhibited low fidelity spark generation. When successfully triggered, sparks in failing cells displayed slow kinetics as Ca2+ spread across dispersed CRUs. During the action potential, these slow sparks protracted and desynchronized the overall Ca2+ transient. Thus, nanoscale RyR reorganization during HF augments Ca2+ leak and slows Ca2+ release kinetics, leading to weakened contraction in this disease.

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The mechanisms underlying the ST segment shifts associated with subendocardial ischemia remain unclear. The aim of this paper is to shed further light on the subject through numerical simulations of these shifts. A realistic three-dimensional model of the ventricles, including fiber rotation and anisotropy, is embedded in a nonhomogeneous torso model. A simplification of the bidomain model is used to calculate only the ST segment shift, assuming known values of the transmembrane potential during the plateau and rest phases. A similar simulation is performed in two dimensions. The simulation results suggest that subendocardial ischemia can be located by ST segment shift on the epicardial and torso surfaces. It is shown that ST elevation is associated with the transmural ischemic boundary, while ST depression is associated with the lateral ischemic boundaries.

The bidomain model, coupled with accurate models of cell membrane kinetics, is generally believed to provide a reasonable basis for numerical simulations of cardiac electrophysiology. Because of changes occurring in very short time intervals and over small spatial domains, discretized versions of these models must be solved on fine computational grids, and small time-steps must be applied. This leads to huge computational challenges that have been addressed by several authors. One popular way of reducing the CPU demands is to approximate the bidomain model by the monodomain model, and thus reducing a two by two set of partial differential equations to one scalar partial differential equation; both of which are coupled to a set of ordinary differential equations modeling the cell membrane kinetics. A reduction in CPU time of two orders of magnitude has been reported. It is the purpose of the present paper to provide arguments that such a reduction is not present when order-optimal numerical methods are applied. Theoretical considerations and numerical experiments indicate that the reduction factor of the CPU requirements from bidomain to monodomain computations, using order-optimal methods, typically is about 10 for simple cell models and less than two for more complex cell models.

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Two mathematical models are part of the foundation of Computational neurophysiology; (a) the Cable equation is used to compute the membrane potential of neurons, and, (b) volume-conductor theory describes the extracellular potential around neurons. In the standard procedure for computing extracellular potentials, the transmembrane currents are computed by means of (a) and the extracellular potentials are computed using an explicit sum over analytical point-current source solutions as prescribed by volume conductor theory. Both models are extremely useful as they allow huge simplifications of the computational efforts involved in computing extracellular potentials. However, there are more accurate, though computationally very expensive, models available where the potentials inside and outside the neurons are computed simultaneously in a self-consistent scheme. In the present work we explore the accuracy of the classical models (a) and (b) by comparing them to these more accurate schemes. The main assumption of (a) is that the ephaptic current can be ignored in the derivation of the Cable equation. We find, however, for our examples with stylized neurons, that the ephaptic current is comparable in magnitude to other currents involved in the computations, suggesting that it may be significant—at least in parts of the simulation. The magnitude of the error introduced in the membrane potential is several millivolts, and this error also translates into errors in the predicted extracellular potentials. While the error becomes negligible if we assume the extracellular conductivity to be very large, this assumption is, unfortunately, not easy to justify a priori for all situations of interest.

Trigger Ca(2+) is considered to be the Ca(2+) current through the L-type Ca(2+) channel (LTCC) that causes release of Ca(2+) from the sarcoplasmic reticulum. However, cell contraction also occurs in the absence of the LTCC current (I(Ca)). In this article, we investigate the contribution of the Na(+)/Ca(2+) exchanger (NCX) to the trigger Ca(2+). Experimental data from rat cardiomyocytes using confocal microscopy indicating that inhibition of reverse mode Na(+)/Ca(2+) exchange delays the Ca(2+) transient by 3-4 ms served as a basis for the mathematical model. A detailed computational model of the dyadic cleft (fuzzy space) is presented where the diffusion of both Na(+) and Ca(2+) is taken into account. Ionic channels are included at discrete locations, making it possible to study the effect of channel position and colocalization. The simulations indicate that if a Na(+) channel is present in the fuzzy space, the NCX is able to bring enough Ca(2+) into the cell to affect the timing of release. However, this critically depends on channel placement and local diffusion properties. With fuzzy space diffusion in the order of four orders of magnitude lower than in water, triggering through LTCC alone was up to 5 ms slower than with the presence of a Na(+) channel and NCX.