Biology subjects

Dokuchaev, A.

Publications and source records attributed to Dokuchaev, A..

3 recordsLinked to original sources

bioRxiv2021

Rotational Activity Around an Obstacle in 2D Cardiac Tissue in Presence of Cellular Heterogeneity

Waves of electrical excitation rotating around an obstacle is one of the important mechanisms of dangerous cardiac arrhythmias occurring in the heart damaged by post-infarction scar. Such a scar also has a border zone around it, which has electrophysiological properties different from the rest of normal myocardial tissue. Spatial patterns of wave rotation in the presence of such tissue heterogeneity are poorly studied. In this paper we perform a comprehensive numerical study of various regimes of rotation of a wave in a plane layer of the ventricular tissue around an obstacle surrounded by a gray zone. We use a TP06 cellular ionic model which reproduces the electrophysiological properties of cardiomyocytes in the left ventricle of human heart. We vary the extent of obstacle and gray zone and study the pattern of wave rotation and its period. We observed different regimes of wave rotation that can be subdivided into several classes: (1) functional rotation and (2) scar rotation regimes, which were identified in the previous studies, and new (3) gray zone rotation regime: where the wave instead of rotation around the obstacle, rotates around the gray zone (an area of tissue heterogeneity) itself. For each class, the period of rotation is determined by different factors, which we discuss and quantify. We also found that due to regional pathological remodeling of myocardial tissue, we can obtain additional regimes associated with dynamical instabilities of two types which may affect or not affect the period of rotation.

physiology↗

bioRxiv2021

Machine Learning prediction of cardiac resynchronisation therapy response from combination of clinical and model-driven data

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSUp 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. ObjectiveThe 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 MethodsRetrospective 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 active poles of 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. ResultsThe 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. 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. ConclusionOur results suggest that combination of clinical and model-driven data increases the accuracy of classification models for CRT outcomes.

physiology↗

bioRxiv2019

A Population Modelling Approach to Studying Age-Related Effects on Excitation-Contraction Coupling in Human Cardiomyocytes

AO_SCPLOWBSTRACTC_SCPLOWAgeing is the dominant risk factor for cardiovascular diseases. A great body of experimental data has been gathered on cellular remodelling in the Ageing myocardium from animals. Very few experimental data are available on age-related changes in the human cardiomyocyte. We have used our combined electromechanical model of the human cardiomyocyte and the population modelling approach to investigate the variability in the response of cardiomyocytes to age-related changes in the model parameters. To generate the model population, we varied nine model parameters and excluded model samples with biomarkers falling outside of the physiological ranges. We evaluated the response to age-related changes in four electrophysiological model parameters reported in the literature: reduction in the density of the K+ transient outward current, maximal velocity of SERCA, and an increase in the density of NaCa exchange current and CaL-type current. The sensitivity of the action potential biomarkers to individual parameter variations was assessed. Each parameter modulation caused an increase in APD, while the sensitivity of the model to changes in GCaL and Vmax_up was much higher than to those in the effects of Gto and KNaCa. Then 60 age-related sets of the four parameters were randomly generated and each set was applied to every model in the control population. We calculated the frequency of model samples with repolarisation anomalies (RA) and the shortening of the electro-mechanical window in the ageing model populations as an arrhythmogenic ageing score. The linear dependence of the score on the deviation of the parameters showed a high determination coefficient with the most significant impact due to the age-related change in the CaL current. The population-based approach allowed us to classify models with low and high risk of age-related RA and to predict risks based on the control biomarkers.

physiology↗