Nonlinear physics of electrical wave propagation in the heart: a review

The beating of the heart is a synchronized contraction of muscle cells (myocytes) that are triggered by a periodic sequence of electrical waves (action potentials) originating in the sino-atrial node and propagating over the atria and the ventricles. Cardiac arrhythmias like atrial and ventricular fibrillation (AF,VF) or ventricular tachycardia (VT) are caused by disruptions and instabilities of these electrical excitations, that lead to the emergence of rotating waves (VT) and turbulent wave patterns (AF,VF). Numerous simulation and experimental studies during the last 20 years have addressed these topics. In this review we focus on the nonlinear dynamics of wave propagation in the heart with an emphasis on the theory of pulses, spirals and scroll waves and their instabilities in excitable media and their application to cardiac modeling. After an introduction into electrophysiological models for action potential propagation, the modeling and analysis of spatiotemporal alternans, spiral and scroll meandering, spiral breakup and scroll wave instabilities like negative line tension and sproing are reviewed in depth and discussed with emphasis on their impact in cardiac arrhythmias.Peer ReviewedPreprin

Patient and Disease-Specific Induced Pluripotent Stem Cells for Discovery of Personalized Cardiovascular Drugs and Therapeutics.

Human induced pluripotent stem cells (iPSCs) have emerged as an effective platform for regenerative therapy, disease modeling, and drug discovery. iPSCs allow for the production of limitless supply of patient-specific somatic cells that enable advancement in cardiovascular precision medicine. Over the past decade, researchers have developed protocols to differentiate iPSCs to multiple cardiovascular lineages, as well as to enhance the maturity and functionality of these cells. Despite significant advances, drug therapy and discovery for cardiovascular disease have lagged behind other fields such as oncology. We speculate that this paucity of drug discovery is due to a previous lack of efficient, reproducible, and translational model systems. Notably, existing drug discovery and testing platforms rely on animal studies and clinical trials, but investigations in animal models have inherent limitations due to interspecies differences. Moreover, clinical trials are inherently flawed by assuming that all individuals with a disease will respond identically to a therapy, ignoring the genetic and epigenomic variations that define our individuality. With ever-improving differentiation and phenotyping methods, patient-specific iPSC-derived cardiovascular cells allow unprecedented opportunities to discover new drug targets and screen compounds for cardiovascular disease. Imbued with the genetic information of an individual, iPSCs will vastly improve our ability to test drugs efficiently, as well as tailor and titrate drug therapy for each patient

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Potassium currents in the heart: functional roles in repolarization, arrhythmia and therapeutics

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Spatiotemporal Control of Human Cardiac Tissue Through Optogenetics

Cardiac arrhythmias are caused by disordered propagation of electrical activity. Progress in understanding and controlling arrhythmias requires novel methods to characterize and control the spatiotemporal propagation of electrical activity. We used patterned illumination of cardiomyocytes derived from optogenetic human induced pluripotent stem cells to create dynamic conduction blocks, and to test spatially extended control schemes. Using this model, we demonstrated the ability to initiate, circumscribe, relocate, and terminate pathologic spiral waves that drive many arrhythmias. When cells were derived from patients with long QT syndrome, longer action potential durations made spiral waves more resistant to termination. This work lays the foundation for personalized models of cardiac injury and disease, and the development of tailored approaches to the management of arrhythmias

Optogenetics: Background, Methodological Advances and Potential Applications for Cardiovascular Research and Medicine

Optogenetics is an elegant approach of precisely controlling and monitoring the biological functions of a cell, group of cells, tissues, or organs with high temporal and spatial resolution by using optical system and genetic engineering technologies. The field evolved with the need to precisely control neurons and decipher neural circuity and has made great accomplishments in neuroscience. It also evolved in cardiovascular research almost a decade ago and has made considerable progress in both in vitro and in vivo animal studies. Thus, this review is written with an objective to provide information on the evolution, background, methodical advances, and potential scope of the field for cardiovascular research and medicine. We begin with a review of literatures on optogenetic proteins related to their origin, structure, types, mechanism of action, methods to improve their performance, and the delivery vehicles and methods to express such proteins on target cells and tissues for cardiovascular research. Next, we reviewed historical and recent literatures to demonstrate the scope of optogenetics for cardiovascular research and regenerative medicine and examined that cardiac optogenetics is vital in mimicking heart diseases, understanding the mechanisms of disease progression and also in introducing novel therapies to treat cardiac abnormalities, such as arrhythmias. We also reviewed optogenetics as promising tools in providing high-throughput data for cardiotoxicity screening in drug development and also in deciphering dynamic roles of signaling moieties in cell signaling. Finally, we put forth considerations on the need of scaling up of the optogenetic system, clinically relevant in vivo and in silico models, light attenuation issues, and concerns over the level, immune reactions, toxicity, and ectopic expression with opsin expression. Detailed investigations on such considerations would accelerate the translation of cardiac optogenetics from present in vitro and in vivo animal studies to clinical therapies

ECG modeling for simulation of arrhythmias in time-varying conditions

The present paper proposes an ECG simulator that advances modeling of arrhythmias and noise by introducing time-varying signal characteristics. The simulator is built around a discrete-time Markov chain model for simulating atrial and ventricular arrhythmias of particular relevance when analyzing atrial fibrillation (AF). Each state is associated with statistical information on episode duration and heartbeat characteristics. Statistical, time-varying modeling of muscle noise, motion artifacts, and the influence of respiration is introduced to increase the complexity of simulated ECGs, making the simulator well suited for data augmentation in machine learning. Modeling of how the PQ and QT intervals depend on heart rate is also introduced. The realism of simulated ECGs is assessed by three experienced doctors, showing that simulated ECGs are difficult to distinguish from real ECGs. Simulator usefulness is illustrated in terms of AF detection performance when either simulated or real ECGs are used to train a neural network for signal quality control. The results show that both types of training lead to similar performance