Differential and combined effects of cardiotrophin-1 and TGF-β<sub>1</sub>on cardiac myofibroblast proliferation and contraction
Bibliographic record
Abstract
Myofibroblasts respond to an array of signals from mitogens and cytokines during the course of wound healing following a myocardial infarction (MI), and these signals may coordinate ventricular myofibroblast proliferation. Furthermore, myofibroblasts are contractile and contribute to wound contraction by imparting mechanical tension on surrounding extracellular matrix. Although TGF-beta(1), CT-1, and PDGF-BB participate in various stages of post-MI wound healing, their combined net effect(s) on myofibroblast function is unknown. We investigated myofibroblast proliferation, expression of cell cycle proteins, and contractile function of cells treated with TGF-beta(1) and/or CT-1. We confirmed that TGF-beta(1) (10 ng/ml) suppresses proliferation of these cells, whereas CT-1 (10 ng/ml) and, for comparative purposes, PDGF-BB (1 ng/ml) treatments were associated with proliferation. Specific TGF-beta(1) treatment ablated CT-1-induced myofibroblast proliferation. TGF-beta(1) effects were specific, as they were suppressed by either TGF-beta-neutralizing antibody or viral Smad7 overexpression. TGF-beta(1) treatment also increased expression of p27 and decreased expression of cyclin E and Cdk2 in primary cells. CT-1 (10 ng/ml) treatment of myofibroblasts had no effect on collagen gel deformation versus controls, whereas TGF-beta(1) (10 ng/ml) and PDGF (10 ng/ml) treatments were associated with significant cell contraction; again, TGF-beta(1)-mediated contraction was unaffected by CT-1. Alone, CT-1 and TGF-beta(1) treatments exert opposing effects on myofibroblast function, whereas in combination TGF-beta(1)-mediated effects supersede those of CT-1 (and PDGF-BB). Thus TGF-beta(1) and CT-1 exert differential effects on myofibroblast proliferation and contraction in vitro, and we suggest that a balance of these effects may be important for the execution of normal cardiac wound healing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".