The effects of stretch and TGFß3 on atrial and ventricular fibroblasts
Bibliographic record
Abstract
Background and objectives: Proliferation and differentiation of cardiac fibroblasts contribute to extracellular matrix (ECM) remodeling and increase the risk of cardiac fibrosis.Atria show greater fibrotic responses than ventricles.To understand the effects of mechanical stimulation, a well-known driver of cardiac fibrosis, and the effects of transforming growth factor -3 (TGF3), an emerging fibrosis regulatory factor, it is important to investigate the biological effects of stretching and TGF3 on atrial versus ventricular fibroblasts.Methods and results: Atrial versus ventricular fibroblasts were compared under a stretch system that applied uniaxial stretch with 10% strain at 1 Hz.Morphological, proliferative and gene expression responses were assessed.The results indicate: (1) Atrial and ventricular fibroblasts had differences in basal gene expression levels and in response to stretch.(2) Stretch increased lysyl oxidase like-2 (LOXL2) and decreased TGF3 mRNA in atrial fibroblasts, but did not affect them in ventricular fibroblasts.Silencing of TGF3 decreased LOXL2 expression and suppressed stretch-induced increase of LOXL2.The addition of TGF3 increased LOXL2 expression, and decreased SMA, collagen 1A1 and collagen 3A1.(3) Fibroblast viability was upregulated by stretch, and RNA interference had no effect on stretch-induced increase of cell viability.However, cell viability was decreased by stimulation with TGF3 at 5 ng/mL.Conclusions: Atrial fibroblasts behave differently from ventricular fibroblasts in response to stretch.Fibroblast proliferation can be activated by stretch, and TGF3 may exert anti-fibrotic effects by preventing cell proliferation.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".