Comparison of Depolarization and Depolarization in Mathematical Models of the Left Ventricle and the Longitudinal Ventricular Slice

Myocardial slices are widely used for cardiac electrophysiology research but correspondence of electrophysiological properties between the cardiac slices and the whole heart has not been studied in details. The aim of this study is to investigate the differences in electrophysiological properties between the left ventricle and the longitudinal ventricular slice passing through the apex using mathematical models. ECG signals and the time of activation and repolarization, repolarization dispersion and dispersion of action potential duration were compared. We have shown that the electrophysiological processes in the ventricle and the longitudinal ventricular slice are quite similar, so we believe that cardiac slices can be used to evaluate global electrophysiological properties of the ventricles. The local differences obtained can be explained by differences in geometry and fiber orientation locally affecting depolarization and repolarization in the myocardium. © 2018 Creative Commons Attribution.Russian Foundation for Basic Research, RFBR: 16-31-60015, 18-31-00401This work was supported by IIF UrB RAS theme #AAAA-A18-118020590031-8, RFE Government Act #211 of March 16, 2013, the Program of the Presidium RAS #27 and RFBR (#16-31-60015, 18-31-00401)

The Concept of Generalised Syllabi of the Discipline «Foreign Language» for Non-Linguistics Major Courses of Study

The paper raises the issue of the relevance of creating generalized syllabi for each enlarged group of majors, when teaching linguistic disciplines of compulsory and optional parts of the curriculum related to the subject area «Foreign Language». The authors show the need for generalized syllabi as being grounded by existing controversies in modern foreign language training. The paper suggests a concept that substantiates the demand for targeted generalized syllabi for this discipline and contains a number of principles, which can help to design their optimal content. The notions of «zones of near and further content and competence integration» are introduced. The content of these zones is revealed, and their role in modelling the key components of generalized syllabi is shown. Integrated foreign language professional communicative competence of an engineering graduate is introduced as a prospective model of foreign language competence. It is regarded as an integrative notion combining traditional components of communicative competence with professional knowledge, skills and experience involved in the process of mastering cross-cultural professional communication. The realization of the concept principles in the content of generalized syllabi aims at the reflection of university graduates’ real foreign language communication needs in their professional activity in learning outcomes in accordance with the FSES HE 3 ++. The authors express certainty that the concept of generalized syllabi will allow to initiate the process of their creation and will become a stimulus to the development of foreign language teaching methodology in a non-linguistics university

Electromechanical coupling in cardiomyocytes depends on its electrotonic interaction with fibroblasts

Cardiac fibroblasts can influence cardiomyocyte electrical activity. Existing mathemati-cal models of fibroblast-cardiomyocyte interaction allow analyzing only electrical responses of effect cardiomyocytes and fibroblasts to their electrical interaction. In our work, we exam-ined fibroblast on the cardiomyocyte mechanics by modelling. We got significant changes in both action potential duration and force generation in the cardiomyocyte depending on the number of fibroblasts connected with it.The work was carried out within the IIF UrB RAS theme No AAAA-A18-118020590031-8, and was supported by the Russian Foundation for Basic Research (18-29-13008, 18-01-00059, 18-015-00368) and by RF Government Act #211 of March 16, 2013 (agreement 02.A03.21.0006)

Machine Learning Prediction of Cardiac Resynchronisation Therapy Response From Combination of Clinical and Model-Driven Data

Background: Up to 30–50% of chronic heart failure patients who underwent cardiac resynchronization therapy (CRT) do not respond to the treatment. Therefore, patient stratification for CRT and optimization of CRT device settings remain a challenge. Objective: The main goal of our study is to develop a predictive model of CRT outcome using a combination of clinical data recorded in patients before CRT and simulations of the response to biventricular (BiV) pacing in personalized computational models of the cardiac electrophysiology. Materials and Methods: Retrospective data from 57 patients who underwent CRT device implantation was utilized. Positive response to CRT was defined by a 10% increase in the left ventricular ejection fraction in a year after implantation. For each patient, an anatomical model of the heart and torso was reconstructed from MRI and CT images and tailored to ECG recorded in the participant. The models were used to compute ventricular activation time, ECG duration and electrical dyssynchrony indices during intrinsic rhythm and BiV pacing from the sites of implanted leads. For building a predictive model of CRT response, we used clinical data recorded before CRT device implantation together with model-derived biomarkers of ventricular excitation in the left bundle branch block mode of activation and under BiV stimulation. Several Machine Learning (ML) classifiers and feature selection algorithms were tested on the hybrid dataset, and the quality of predictors was assessed using the area under receiver operating curve (ROC AUC). The classifiers on the hybrid data were compared with ML models built on clinical data only. Results: The best ML classifier utilizing a hybrid set of clinical and model-driven data demonstrated ROC AUC of 0.82, an accuracy of 0.82, sensitivity of 0.85, and specificity of 0.78, improving quality over that of ML predictors built on clinical data from much larger datasets by more than 0.1. Distance from the LV pacing site to the post-infarction zone and ventricular activation characteristics under BiV pacing were shown as the most relevant model-driven features for CRT response classification. Conclusion: Our results suggest that combination of clinical and model-driven data increases the accuracy of classification models for CRT outcomes. Copyright © 2021 Khamzin, Dokuchaev, Bazhutina, Chumarnaya, Zubarev, Lyubimtseva, Lebedeva, Lebedev, Gurev and Solovyova.This work was supported by Russian Science Foundation grant no. 19-14-00134

Postindustrial Model of Russia’s Development

В данной статье в обзорной форме рассматривается мегапроект, позволяющий России совершить качественный переход в своем развитии в масштаб седьмого постиндустриального технологического уклада. Важным фактором седьмого постиндустриального технологического уклада является наука «Управление», отличающаяся от понятий «наука управления» и «теория менеджмента», представляющих комплекс мер, стратегий по ведению хозяйства, управленческий инструментарий и т. д. Создание науки «Управление» и производительных сил седьмого постиндустриального технологического уклада - виртуальных технологий и инновационной экономики - составляет содержание мегапроекта. Авторы статьи дают аргументированное обоснование мегапроекта и приводят краткую характеристику указанных производительных сил

Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy

Introduction: The 30–50% non-response rate to cardiac resynchronization therapy (CRT) calls for improved patient selection and optimized pacing lead placement. The study aimed to develop a novel technique using patient-specific cardiac models and machine learning (ML) to predict an optimal left ventricular (LV) pacing site (ML-PS) that maximizes the likelihood of LV ejection fraction (LVEF) improvement in a given CRT candidate. To validate the approach, we evaluated whether the distance DPS between the clinical LV pacing site (ref-PS) and ML-PS is associated with improved response rate and magnitude. Materials and methods: We reviewed retrospective data for 57 CRT recipients. A positive response was defined as a more than 10% LVEF improvement. Personalized models of ventricular activation and ECG were created from MRI and CT images. The characteristics of ventricular activation during intrinsic rhythm and biventricular (BiV) pacing with ref-PS were derived from the models and used in combination with clinical data to train supervised ML classifiers. The best logistic regression model classified CRT responders with a high accuracy of 0.77 (ROC AUC = 0.84). The LR classifier, model simulations and Bayesian optimization with Gaussian process regression were combined to identify an optimal ML-PS that maximizes the ML-score of CRT response over the LV surface in each patient. Results: The optimal ML-PS improved the ML-score by 17 ± 14% over the ref-PS. Twenty percent of the non-responders were reclassified as positive at ML-PS. Selection of positive patients with a max ML-score >0.5 demonstrated an improved clinical response rate. The distance DPS was shorter in the responders. The max ML-score and DPS were found to be strong predictors of CRT response (ROC AUC = 0.85). In the group with max ML-score > 0.5 and DPS< 30 mm, the response rate was 83% compared to 14% in the rest of the cohort. LVEF improvement in this group was higher than in the other patients (16 ± 8% vs. 7 ± 8%). Conclusion: A new technique combining clinical data, personalized heart modelling and supervised ML demonstrates the potential for use in clinical practice to assist in optimizing patient selection and predicting optimal LV pacing lead position in HF candidates for CRT. Copyright © 2023 Dokuchaev, Chumarnaya, Bazhutina, Khamzin, Lebedeva, Lyubimtseva, Zubarev, Lebedev and Solovyova.Russian Science Foundation, RSF: 19-14-00134This work was supported by Russian Science Foundation Grant No. 19-14-00134

Mathematical modelling of the mechano-electric coupling in the human cardiomyocyte electrically connected with fibroblasts

Cardiac fibroblasts are interspersed within mammalian cardiac tissue. Fibroblasts are mechanically passive; however, they may communicate electrically with cardiomyocytes via gap junctions and thus affect the electrical and mechanical activity of myocytes. Several in-silico studies at both cellular (0D) and ventricular (3D) levels analysed the effects of fibroblasts on the myocardial electrical function. However, none of them addressed possible effects of fibroblast-myocyte electrical coupling to cardiomyocyte mechanical activity. In this paper, we propose a mathematical model for studying both electrical and mechanical responses of the human cardiomyocyte to its electrotonic interaction with cardiac fibroblasts. Our simulations have revealed that electrotonic interaction with fibroblasts affects not only the mechanical activity of the cardiomyocyte, comprising either moderate or significant reduction of contractility, but also the mechano-calcium and mechano-electric feedback loops, and all these effects are enhanced as the number of coupled fibroblasts is increased. Obtained results suggest that moderate values of the myocyte-fibroblast gap junction conductance (less than 1 nS) can be attributed to physiological conditions, contrasting to the higher values (2 nS and higher) proper rather for pathological situations (e.g. for infarct and/or border zones), since all mechanical indexes falls down dramatically in the case of such high conductance