The effects of cardiac stretch on atrial fibroblasts: analysis of the evidence and potential role in atrial fibrillation
Bibliographic record
Abstract
Atrial fibrillation (AF) is an important clinical problem. Chronic pressure/volume overload of the atria promotes AF, particularly via enhanced extracellular matrix (ECM) accumulation manifested as tissue fibrosis. Loading of cardiac cells causes cell stretch that is generally considered to promote fibrosis by directly activating fibroblasts, the key cell type responsible for ECM production. The primary purpose of this article is to review the evidence regarding direct effects of stretch on cardiac fibroblasts, specifically: (i) the similarities and differences among studies in observed effects of stretch on cardiac fibroblast function; (ii) the signalling pathways implicated; and (iii) the factors that affect stretch-related phenotypes. Our review summarizes the most important findings and limitations in this area and gives an overview of clinical data and animal models related to cardiac stretch, with particular emphasis on the atria. We suggest that the evidence regarding direct fibroblast activation by stretch is weak and inconsistent, in part because of variability among studies in key experimental conditions that govern the results. Further work is needed to clarify whether, in fact, stretch induces direct activation of cardiac fibroblasts and if so, to elucidate the determining factors to ensure reproducible results. If mechanical load on fibroblasts proves not to be clearly profibrotic by direct actions, other mechanisms like paracrine influences, the effects of systemic mediators and/or the direct consequences of myocardial injury or death, might account for the link between cardiac stretch and fibrosis. Clarity in this area is needed to improve our understanding of AF pathophysiology and assist in therapeutic development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".