scholarly journals In-silico analysis of aging mechanisms of action potential remodeling in human atrial cardiomyocites

2020 ◽  
Vol 22 ◽  
pp. 01025
Author(s):  
Tatyana Nesterova ◽  
Dmitry Shmarko ◽  
Konstantin Ushenin ◽  
Olga Solovyova
Keyword(s):  
Experimental Data ◽  
Atrial Fibrillation ◽  
Action Potential ◽  
In Silico ◽  
Antiarrhythmic Drugs ◽  
Model Parameters ◽  
Experimental Conditions ◽  
Human Cardiomyocytes ◽  
Atrial Cardiomyocytes ◽  
Age Related

Electrophysiology of cardiomyocytes changes with aging. Agerelated ionic remodeling in cardiomyocytes may increase the incidence and prevalence of atrial fibrillation (AF) in the elderly and affect the efficiency of antiarrhythmic drugs. There is the deep lack of experimental data on an action potential and transmembrane currents recorded in the healthy human cardiomyocytes of different age. Experimental data in mammals is also incomplete and often contradicting depending on the experimental conditions. In this in-silico study, we used a population of ionic models of human atrial cardiomyocytes to transfer data on the age- related ionic remodeling in atrial cardiomyocytes from canines and mice to predict possible consequences for human cardiomyocyte activity. Based on experimental data, we analyzes two hypotheses on the aging effect on the ionic currents using two age-related sets of varied model parameters and evaluated corresponding changes in action potential morphology with aging. Using the two populations of aging models, we analyzed the agedependent sensitivity of atrial cardiomyocytes to Dofetilide which is one of the antiarrhythmic drugs widely used in patients with atrial fibrillation.

scholarly journals Electrophysiological Biomarkers for Age-Related Changes in Human Atrial Cardiomyocytes: In Silico Study

2020 ◽  
Vol 31 ◽  
pp. 01004 ◽  
Author(s):  
Tatyana Nesterova ◽  
Konstantin Ushenin ◽  
Dmitry Shmarko ◽  
Olga Solovyova
Keyword(s):  
Experimental Data ◽  
Action Potential ◽  
Action Potential Duration ◽  
Cellular Response ◽  
Experimental Studies ◽  
Model Parameters ◽  
Human Cardiomyocytes ◽  
Atrial Cardiomyocytes ◽  
Age Related ◽  
Age Related Changes

Age-related changes in human cardiomyocytes are closely related to cardiac diseases, especially atrial fibrillation. Restricted availability of biological preparations from the human atrial myocardium complicates experimental studies on the aging processes in cardiomyocytes. In this preliminary study, we used available experimental data on the age-related changes in ionic conductances in canine atrial cardiomyocytes to predict possible consequences of similar remodeling in humans using two mathematical models (Courtemanche98 and Maleckar09) of human atrial cardiomyocytes. The study was performed using the model population approach, allowing one to assess variability in the cellular response to different interventions affecting model parameters. Here, this approach was used to evaluate the effects of age-related parameter modulation on action potential biomarkers in the two models. Simulation results show a significant decrease in the action potential duration and membrane potential at 20% of the action potential duration in aging. These model predictions are consistent with experimental data from mammalians. The action potential characteristics are shown to serve as notable biomarkers of age-related electrophysiological remodeling in human atrial cardiomyocytes. A comparison of the two models shows different behavior in the prediction of repolarization abnormalities.

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

2019 ◽  
Author(s):  
Arsenii Dokuchaev ◽  
Svyatoslav Khamzin ◽  
Olga Solovyova
Keyword(s):  
Experimental Data ◽  
Transient Outward Current ◽  
Model Parameters ◽  
Maximal Velocity ◽  
Population Modelling ◽  
Individual Parameter ◽  
Human Cardiomyocytes ◽  
Age Related ◽  
Age Related Changes ◽  
Modelling Approach

AbstractAgeing 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.

Uncertainty Quantification of Viscoplastic Parameters for Grade 91 Steel Through Bayesian Analysis

2020 ◽  
Author(s):  
Aritra Chakraborty ◽  
M. C. Messner ◽  
T.-L. Sham
Keyword(s):  
Experimental Data ◽  
High Temperature ◽  
Initial Guess ◽  
Mcmc Methods ◽  
Model Parameters ◽  
Parameter Uncertainties ◽  
Experimental Conditions ◽  
Materials Used ◽  
Grade 91 ◽  
Effective Component

Abstract Calibrating inelastic models for high temperature materials used in advanced reactor heat exchangers is a critical aspect in accurately predicting their deformation behavior under different loading conditions, and thus determining the corresponding failure times. The experimental data against which these models are calibrated often contains a wide degree of variability caused by heat-to-heat material property variations and general experimental uncertainty. Most often, model calibration is done against mean of these experimental data without considering this variability. In this work we aim to capture the bounds of the viscoplastic parameter uncertainties that enclose this observed scatter in the experimental data using Bayesian Markov Chain Monte Carlo (MCMC) methods. Bayesian inference provides a probabilistic framework that allows to coherently quantify parameter uncertainties based on some prior parameter distributions and the available data. To perform the statistical Bayesian MCMC analysis, a pre-calibrated model, fitted against mean of the experimental data, is used as an initial guess for the prior distribution and bounds, while further sampling is done using Meteropolis–Hastings algorithm for four Markov chains in tandem, to finally obtain the posterior distribution of the model parameters. Since different inelastic parameters are sensitive to different tests, data from multiple experimental conditions (tensile, and creep) are combined to capture the bounds in all the parameters. The developed statistical model reasonably captures the scatter observed in the experimental data. Quantifying uncertainty in inelastic models will improve high temperature engineering design practice and lead to safer, more effective component designs.

Epithelial transport parameters: an analysis of experimental strategies

1983 ◽  
Vol 218 (1212) ◽  
pp. 309-329 ◽  
Keyword(s):  
Experimental Data ◽  
Epithelial Transport ◽  
Flux Ratio ◽  
Model Parameters ◽  
Acta Physiol ◽  
Transport Parameters ◽  
Na Transport ◽  
Experimental Conditions ◽  
System Parameters ◽  
Experimental Strategies

A set of experiments was simulated on a computer version of the Koefoed-Johnsen & Ussing model for high-resistance epithelia. The results obtained were analysed according to procedures commonly applied to the analyses of experimental data and interpreted in terms of the model parameters. Although the computer model encodes a stoichiometry of 3:2 for Na-K exchange through the Na pump, the simulation of published experimental procedures yields different figures in almost every case. We show that E Na as originally defined by Ussing & Zerahn ( Acta physiol. scand . 23, 110-127 (1951)) and as obtained from flux-ratio experiments has different values under different experimental conditions with unchanged system parameters and that it is distinct from E Na measured by other methods. We also show that unless the pump is saturated with internal Na an increase in the rate of pumping cannot cause a substantial increase in the rate of transepithelial Na transport.

scholarly journals Artificial Intelligence-Driven Algorithm for Drug Effect Prediction on Atrial Fibrillation: An in silico Population of Models Approach

2021 ◽  
Vol 12 ◽  
Author(s):  
Ana Maria Sanchez de la Nava ◽  
Ángel Arenal ◽  
Francisco Fernández-Avilés ◽  
Felipe Atienza
Keyword(s):  
Artificial Intelligence ◽  
Atrial Fibrillation ◽  
In Silico ◽  
Antiarrhythmic Drugs ◽  
Ionic Channel ◽  
Pattern Identification ◽  
Pore Channel ◽  
Atrial Size ◽  
And Diffusion ◽  
Fold Cross Validation

Background: Antiarrhythmic drugs are the first-line treatment for atrial fibrillation (AF), but their effect is highly dependent on the characteristics of the patient. Moreover, anatomical variability, and specifically atrial size, have also a strong influence on AF recurrence.Objective: We performed a proof-of-concept study using artificial intelligence (AI) that enabled us to identify proarrhythmic profiles based on pattern identification from in silico simulations.Methods: A population of models consisting of 127 electrophysiological profiles with a variation of nine electrophysiological variables (GNa, INaK, GK1, GCaL, GKur, IKCa, [Na]ext, and [K]ext and diffusion) was simulated using the Koivumaki atrial model on square planes corresponding to a normal (16 cm2) and dilated (22.5 cm2) atrium. The simple pore channel equation was used for drug implementation including three drugs (isoproterenol, flecainide, and verapamil). We analyzed the effect of every ionic channel combination to evaluate arrhythmia induction. A Random Forest algorithm was trained using the population of models and AF inducibility as input and output, respectively. The algorithm was trained with 80% of the data (N = 832) and 20% of the data was used for testing with a k-fold cross-validation (k = 5).Results: We found two electrophysiological patterns derived from the AI algorithm that was associated with proarrhythmic behavior in most of the profiles, where GK1 was identified as the most important current for classifying the proarrhythmicity of a given profile. Additionally, we found different effects of the drugs depending on the electrophysiological profile and a higher tendency of the dilated tissue to fibrillate (Small tissue: 80 profiles vs Dilated tissue: 87 profiles).Conclusion: Artificial intelligence algorithms appear as a novel tool for electrophysiological pattern identification and analysis of the effect of antiarrhythmic drugs on a heterogeneous population of patients with AF.

scholarly journals In-silico study of age-related ionic remodeling in human ventricular cardiomyocytes

2020 ◽  
Vol 22 ◽  
pp. 01024
Author(s):  
Arsenii Dokuchaev ◽  
Svyatoslav Khamzin ◽  
Olga Solovyova
Keyword(s):  
Outward Current ◽  
Ionic Currents ◽  
Population Based ◽  
Control Population ◽  
Transient Outward Current ◽  
Model Parameters ◽  
Human Cardiomyocytes ◽  
Ventricular Cardiomyocytes ◽  
Age Related ◽  
Ageing Model

Ageing is one of the dominant risk factors for cardiovascular diseases. A large number of experimental data is collected on the cellular remodeling in the ageing myocardium from mammals, but very little is known about the human cardiomyocytes. We used a combined electro-mechanical model of human ventricular cardiomyocytes and a population of models approach to investigate the variability in the response of cardiomyocytes to age-related changes in model parameters of the ionic currents. To generate a control model population, we varied 9 ionic parameters and excluded model samples with biomarkers of cellular action potential (AP) and Ca2+ transient (CT) falling outside the physiological ranges. Using the control population of models, we evaluated the response to age-related reduction in the K+ transient outward current, SERCA pump, and an increase in the Na+Ca2+ exchange current and L-type Ca2+ current. Then, we randomly generated 60 age-related sets of the 4 parameters and applied each set to every model in the control population. We showed an increase in the frequency of repolarization anomalies (RA) and critical AP prolongation in the ageing model populations suggesting arrhythmogenic effects of the ionic remodeling. The population based approach allowed us to assess the pro-arrhythmic contribution of the ionic parameters in ageing cardiomyocytes.

scholarly journals Fibrotic Remodeling during Persistent Atrial Fibrillation: In Silico Investigation of the Role of Calcium for Human Atrial Myofibroblast Electrophysiology

Cells ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 2852
Author(s):  
Jorge Sánchez ◽  
Beatriz Trenor ◽  
Javier Saiz ◽  
Olaf Dössel ◽  
Axel Loewe
Keyword(s):  
Atrial Fibrillation ◽  
In Silico ◽  
Cardiac Tissue ◽  
Persistent Atrial Fibrillation ◽  
Distribution Patterns ◽  
Tissue Level ◽  
Resting Membrane Potential ◽  
Model Parameters

During atrial fibrillation, cardiac tissue undergoes different remodeling processes at different scales from the molecular level to the tissue level. One central player that contributes to both electrical and structural remodeling is the myofibroblast. Based on recent experimental evidence on myofibroblasts’ ability to contract, we extended a biophysical myofibroblast model with Ca2+ handling components and studied the effect on cellular and tissue electrophysiology. Using genetic algorithms, we fitted the myofibroblast model parameters to the existing in vitro data. In silico experiments showed that Ca2+ currents can explain the experimentally observed variability regarding the myofibroblast resting membrane potential. The presence of an L-type Ca2+ current can trigger automaticity in the myofibroblast with a cycle length of 799.9 ms. Myocyte action potentials were prolonged when coupled to myofibroblasts with Ca2+ handling machinery. Different spatial myofibroblast distribution patterns increased the vulnerable window to induce arrhythmia from 12 ms in non-fibrotic tissue to 22 ± 2.5 ms and altered the reentry dynamics. Our findings suggest that Ca2+ handling can considerably affect myofibroblast electrophysiology and alter the electrical propagation in atrial tissue composed of myocytes coupled with myofibroblasts. These findings can inform experimental validation experiments to further elucidate the role of myofibroblast Ca2+ handling in atrial arrhythmogenesis.

scholarly journals Computational modeling of atrial fibrillation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Oh-Seok Kwon ◽  
Inseok Hwang ◽  
Hui-Nam Pak
Keyword(s):  
Artificial Intelligence ◽  
Atrial Fibrillation ◽  
Catheter Ablation ◽  
Computational Modeling ◽  
Computer Science ◽  
Precision Medicine ◽  
In Silico ◽  
Antiarrhythmic Drugs ◽  
Ablation Lesion ◽  
Aging Society

AbstractWith the aging society, the prevalence of atrial fibrillation (AF) continues to increase. Nevertheless, there are still limitations in antiarrhythmic drugs (AAD) or catheter interventions for AF. If it is possible to predict the outcome of AF management according to various AADs or ablation lesion sets through computational modeling, it will be of great clinical help. AF computational modeling has been utilized for in-silico arrhythmia research and enabled high-density entire chamber mapping, reproducible condition control, virtual intervention, not possible clinically or experimentally, in-depth mechanistic research. With the recent development of computer science and technology, more sophisticated and faster computational modeling has become available for clinical application. In particular, it can be applied to determine the extra-PV target of persistent AF catheter ablation or to select the AAD with the best effect. AF computational modeling combined with artificial intelligence is expected to contribute to precision medicine for more diverse uses in the future. Therefore, in this review, we will deal with the history, development, and various applications of computation modeling.

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