Transforming Growth Factor‐β Regulates Valve Interstitial Cell Growth in Vitro
Bibliographic record
Abstract
Background The effect of transforming growth factor beta (TGF‐β) on VIC growth independent of injury is poorly understood. We showed that in the early stages of VIC wound repair TGF‐β promotes VIC proliferation and repair (A. Liu et al. Am J Pathol 2008; 173:1275–1285). In contrast, we found that under normal non‐wound conditions TGF‐β decreases cell number, alters cell morphology, and increases cell adhesion. In this study we set out to investigate the molecular mechanisms responsible for the latter observations. Materials and Methods SB431542 is a potent inhibitor of the TGF‐β receptor I. Low density cultures were treated with TGF‐β or SB431542 and their respective controls. The extent of retinoblastoma protein (pRb) phosphorylation and expression of cyclin D1, CDK4, and p27 were compared using Western Blotting. Results TGF‐β treatment inhibits VIC proliferation by under‐phosphorylation of p‐Rb at Ser807/811, which could be reversed by receptor signaling inhibition. There is also a concomitant downregulation of cyclin D1/CDK4 and upregulation of their inhibitor, p27. The decrease in cell number caused by exogenous TGF‐β can be abolished by the inhibitor. Conclusion TGF‐β regulates VIC growth in vitro by inhibiting proliferation in low‐density monolayer while stimulating proliferation at the wound edge following injury.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".