ArticlePLoS computational biology2019
Self-organization of conducting pathways explains electrical wave propagation in cardiac tissues with high fraction of non-conducting cells.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 34 citations in OpenAlex.
- Paracrine Induction of Cardiomyogenic Differentiation in Patient-Specific MSCs Using Conditioned Medium from iPSC-CMs.Biomedicines · 2026Article
- Smart Optogenetics for Real-Time Automated Control of Cardiac Electrical Activity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Spatially heterogeneous ionic remodelling promotes wavefront instability in simulated atrial tissue.Frontiers in network physiology · 2026Article
- Refining fibroblast-to-cardiomyocyte transdifferentiation protocols to explore emergent self-organization in cardiac cultures.PloS one · 2026Article
- Advanced Cardiovascular Toxicity Screening: Integrating Human iPSC-Derived Cardiomyocytes with 2D In Silico Models.Cardiovascular toxicology · 2025Article
- Biomimetic Cardiac Tissue Models for In Vitro Arrhythmia Studies.Biomimetics (Basel, Switzerland) · 2023Review
- The role of the Cx43/Cx45 gap junction voltage gating on wave propagation and arrhythmogenic activity in cardiac tissue.Scientific reports · 2023Article
- Novel Molecular Vehicle-Based Approach for Cardiac Cell Transplantation Leads to Rapid Electromechanical Graft-Host Coupling.International journal of molecular sciences · 2023Article
- Polymer Kernels as Compact Carriers for Suspended Cardiomyocytes.Micromachines · 2022Article
- Multiparametric analysis of geometric features of fibrotic textures leading to cardiac arrhythmias.Scientific reports · 2021Article
- Percolation and tortuosity in heart-like cells.Scientific reports · 2021Article
- Optimizing the Use of iPSC-CMs for Cardiac Regeneration in Animal Models.Animals : an open access journal from MDPI · 2020Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiac fibrosis occurs in many forms of heart disease and is considered to be one of the main arrhythmogenic factors. Regions with a high density of fibroblasts are likely to cause blocks of wave propagation that give rise to dangerous cardiac arrhythmias. Therefore, studies of the wave propagation through these regions are very important, yet the precise mechanisms leading to arrhythmia formation in fibrotic cardiac tissue remain poorly understood. Particularly, it is not clear how wave propagation is organized at the cellular level, as experiments show that the regions with a high percentage of fibroblasts (65-75%) are still conducting electrical signals, whereas geometric analysis of randomly distributed conducting and non-conducting cells predicts connectivity loss at 40% at the most (percolation threshold). To address this question, we used a joint in vitro-in silico approach, which combined experiments in neonatal rat cardiac monolayers with morphological and electrophysiological computer simulations. We have shown that the main reason for sustainable wave propagation in highly fibrotic samples is the formation of a branching network of cardiomyocytes. We have successfully reproduced the morphology of conductive pathways in computer modelling, assuming that cardiomyocytes align their cytoskeletons to fuse into cardiac syncytium. The electrophysiological properties of the monolayers, such as conduction velocity, conduction blocks and wave fractionation, were reproduced as well. In a virtual cardiac tissue, we have also examined the wave propagation at the subcellular level, detected wavebreaks formation and its relation to the structure of fibrosis and, thus, analysed the processes leading to the onset of arrhythmias.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.