Supervised Machine Learning for Classification of the Electrophysiological Effects of Chronotropic Drugs on Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes

Supervised machine learning can be used to predict which drugs human cardiomyocytes have been exposed to. Using electrophysiological data collected from human cardiomyocytes with known exposure to different drugs, a supervised machine learning algorithm can be trained to recognize and classify cells that have been exposed to an unknown drug. Furthermore, the learning algorithm provides information on the relative contribution of each data parameter to the overall classification. Probabilities and confidence in the accuracy of each classification may also be determined by the algorithm. In this study, the electrophysiological effects of β–adrenergic drugs, propranolol and isoproterenol, on cardiomyocytes derived from human induced pluripotent stem cells (hiPS-CM) were assessed. The electrophysiological data were collected using high temporal resolution 2-photon microscopy of voltage sensitive dyes as a reporter of membrane voltage. The results demonstrate the ability of our algorithm to accurately assess, classify, and predict hiPS-CM membrane depolarization following exposure to chronotropic drugs

Induced Pluripotent Stem Cell-Derived Disease Model for Catecholaminergic Polymorphic Ventricular Tachycardia

Human induced pluripotent stem cells (hiPSCs) offer significant opportunities for cardiac research. With this technology, it is possible to create patient-specific stem cell lines and differentiate them into cardiomyocytes for cardiac research. hiPSC technology has created many expectations for new therapeutic possibilities, and it holds promise for use in drug-testing platforms and in patient-specific drug therapy optimization, as well as later in regenerative medicine.Catecholaminergic polymorphic ventricular tachycardia (CPVT) is an inherited, highly lethal arrhythmogenic cardiac disorder. It is primarily caused by cardiac ryanodine receptor gene (RyR2) mutations that result in abnormal calcium release from the sarcoplasmic reticulum to the cytosol, leading to the generation of afterdepolarizations and triggered activity. The estimated clinical prevalence of CPVT is 1:10000. Intracellular calcium ions are crucial to the function of the heart muscle, and disturbances in this process can have fatal consequences, as observed in CPVT. Understanding the mechanisms of arrhythmia and the role of intracellular calcium in CPVT pathophysiology is important for improving disease prevention, diagnosis, and treatment.The main objective of this work was to develop and characterize models of cardiac cells and to develop and improve techniques for studying electrical field stimulation and calcium cycling of cardiomyocytes. Utilizing electrical field stimulation, the orientation and maturation of neonatal rat cardiomyocytes and the increase in the beating rate of an in vitro disease model for CPVT were studied. For the cell model of CPVT, human iPSC-derived cardiomyocytes were obtained from CPVT patients carrying RyR2 mutations. These iPSCs disease models were used to study the disease mechanisms of CPVT, mutation-specific differences in intracellular calcium cycling and the effect of antiarrhythmic treatment of the cells. Mechanistic insights regarding CPVT arrhythmias and drug responses were also validated in the index patients. Additionally, a new calcium cycling analysis software tool was developed for characterizing abnormal intracellular calcium transients of disease-specific cardiomyocytes.The results of this work demonstrate that patient-specific iPSC-derived cardiomyocytes corresponded to the clinical phenotype in both the pathophysiology and drug responses of CPVT and encourages the continuation of disease modeling utilizing iPSCs. These studies also presented a new mechanism for arrhythmias in CPVT. These findings encourage the translation of findings in basic research to benefit patients in clinical practice, e.g., in the form of potentially new medications

Analyzing Sequential Pattern Mining to Detect Calcium Peaks in Cardiomyocytes Data

This study examines sequential pattern mining and its applications in various fields. The previous research was conducted by examining signal data, from which calcium peaks were automatically detected and classified. Before the implementation of sequential pattern mining approach to find out patterns from a dataset of 102 signals, association rule mining, frequent itemsets, Apriori algorithm, and rule generation were explored. Sequential pattern mining, including time constraints, are defined, before examining a knowledge-assisted sequential pattern analysis, from which certain points are considered, such as what is a sequential itemset. The implementation phase consists of calculating what constitutes a candidate itemset. The findings are modified to work with a sequential rule mining algorithm, and the results are discussed afterwards

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 250)

This bibliography lists 265 reports, articles and other documents introduced into the NASA scientific and technical information system in September 1983

Computational Physiology

This open access volume compiles student reports from the 2021 Simula Summer School in Computational Physiology. Interested readers will find herein a number of modern approaches to modeling excitable tissue. This should provide a framework for tools available to model subcellular and tissue-level physiology across scales and scientific questions. In June through August of 2021, Simula held the seventh annual Summer School in Computational Physiology in collaboration with the University of Oslo (UiO) and the University of California, San Diego (UCSD). The course focuses on modeling excitable tissues, with a special interest in cardiac physiology and neuroscience. The majority of the school consists of group research projects conducted by Masters and PhD students from around the world, and advised by scientists at Simula, UiO and UCSD. Each group then produced a report that addreses a specific problem of importance in physiology and presents a succinct summary of the findings. Reports may not necessarily represent new scientific results; rather, they can reproduce or supplement earlier computational studies or experimental findings. Reports from eight of the summer projects are included as separate chapters. The fields represented include cardiac geometry definition (Chapter 1), electrophysiology and pharmacology (Chapters 2–5), fluid mechanics in blood vessels (Chapter 6), cardiac calcium handling and mechanics (Chapter 7), and machine learning in cardiac electrophysiology (Chapter 8)

Characterization and Clinical Management of Dilated Cardiomyopathy

Dilated cardiomyopathy (DCM) is a particular phenotype of non-ischemic systolic heart failure, frequently recognizing a genetic background and affecting relatively young patients with few comorbidities. Nowadays, long-term survival of DCM patients has been markedly improved due to an early diagnosis and uninterrupted and tailored follow-up under constant optimal medical and non-pharmacological evidence-based treatments. Nevertheless, DCM is still one of the most common causes of heart transplantation in the western world. Clinical management requires an integrated and systematic use of diagnostic tools and a deeper investigation of the basic mechanisms underlying the disease. However, several emerging issues remain debated. Specifically, the genotype–phenotype correlation, the role of advanced imaging techniques and genetic testing, the lack of appropriate risk stratification models, the need for a multiparametric and multidisciplinary approach for device implantation, and a continuous reclassification of the disease during follow-up remain challenging issues in clinical practice. Therefore, the aim of this Special Issue is to shed the light on the most recent advancements in characterization and clinical management of DCM in order to unveil the conundrum of this particular disease

Computational Physiology

This open access volume compiles student reports from the 2021 Simula Summer School in Computational Physiology. Interested readers will find herein a number of modern approaches to modeling excitable tissue. This should provide a framework for tools available to model subcellular and tissue-level physiology across scales and scientific questions. In June through August of 2021, Simula held the seventh annual Summer School in Computational Physiology in collaboration with the University of Oslo (UiO) and the University of California, San Diego (UCSD). The course focuses on modeling excitable tissues, with a special interest in cardiac physiology and neuroscience. The majority of the school consists of group research projects conducted by Masters and PhD students from around the world, and advised by scientists at Simula, UiO and UCSD. Each group then produced a report that addreses a specific problem of importance in physiology and presents a succinct summary of the findings. Reports may not necessarily represent new scientific results; rather, they can reproduce or supplement earlier computational studies or experimental findings. Reports from eight of the summer projects are included as separate chapters. The fields represented include cardiac geometry definition (Chapter 1), electrophysiology and pharmacology (Chapters 2–5), fluid mechanics in blood vessels (Chapter 6), cardiac calcium handling and mechanics (Chapter 7), and machine learning in cardiac electrophysiology (Chapter 8)

Diagnostic Challenges in Sports Cardiology

The foundations of sports cardiology include promoting physical activity and providing a safe environment for training and competition for all athletes at all levels, from professional to recreational. To combine these two aims, reliable tools to perform preparticipation screenings are needed. Moreover, those at high risk of potentially life-threatening events should be advised to limit their training load, while others should be reassured that there is no exercise-related cardiovascular risk. We are currently witnessing the advent of new portable devices for remote and mobile heart monitoring and several new and promising biochemical markers, which can support athletes’ diagnostic processes. In this Special Issue of the Diagnostics journal entitled “Diagnostic Challenges in Sports Cardiology”, we present a series of 13 manuscripts, including eight original works, three reviews, and two case reports, which give a glimpse into the current research topics in the area of sports cardiology