Molecular determinants of cardiac fibroblast electrical function and therapeutic implications for atrial fibrillation
Bibliographic record
Abstract
Cardiac fibroblasts account for about 75% of all cardiac cells, but because of their small size contribute only ∼10-15% of total cardiac cell volume. They play a crucial role in cardiac pathophysiology. For a long time, it has been recognized that fibroblasts and related cell types are the principal sources of extracellular matrix (ECM) proteins, which organize cardiac cellular architecture. In disease states, fibroblast production of increased quantities of ECM proteins leads to tissue fibrosis, which can impair both mechanical and electrical function of the heart, contributing to heart failure and arrhythmogenesis. Atrial fibrosis is known to play a particularly important role in atrial fibrillation (AF). This review article focuses on recent advances in understanding the molecular electrophysiology of cardiac fibroblasts. Cardiac fibroblasts express a variety of ion channels, in particular voltage-gated K(+) channels and non-selective cation channels of the transient receptor potential (TRP) family. Both K(+) and TRP channels are important determinants of fibroblast function, with TRP channels acting as Ca(2+)-entry pathways that stimulate fibroblast differentiation into secretory myofibroblast phenotypes producing ECM proteins. Fibroblasts can couple to cardiomyocytes and substantially affect their cellular electrical properties, including conduction, resting potential, repolarization, and excitability. Co-cultured preparations of cardiomyocytes and fibroblasts generate arrhythmias by a variety of mechanisms, including spontaneous impulse formation and rotor-driven reentry. In addition, the excess ECM proteins produced by fibroblasts can interrupt cardiomyocyte-bundle continuity, leading to local conduction disturbances and reentrant arrhythmias. A better understanding of the electrical properties of fibroblasts should lead to an improved comprehension of AF pathophysiology and a variety of novel targets for antiarrhythmic intervention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".