The Context‐dependent Effect of Transforming Growth Factor Beta on Valve Interstitial Cell Growth in Vitro
Bibliographic record
Abstract
Background Valve interstitial cells (VICs) are the most prevalent cell type in heart valves. We showed that in the early stages of VIC wound repair transforming growth factor beta (TGF‐β) promotes VIC proliferation and repair (A. Liu et al. Am J Pathol 2008; 173:1275–1285). However, the effect of TGF‐β on VIC growth independent of injury is poorly understood. Materials and Methods We used normal subconfluent VIC monolayer cultures to test the hypothesis that TGF‐β enhances VIC proliferation. Control and TGF‐β treated VICs were plated at low density. Cell number and proliferation (bromodeoxyuridine) were quantified. Cell morphology was observed and the extent of VIC adhesion to the substratum was quantified using a trypsin adhesion assay. Results TGF‐β significantly decreased cell number and altered cell morphology as cells were mostly rhomboid‐shaped as opposed to spindle‐shaped. Cell proliferation was significantly inhibited and treated cells showed significantly more adhesion to the substratum, which may contribute to the observed growth inhibition. Conclusion The effect of TGF‐β on VIC growth is context dependent, allowing it to play a dual role by preventing excessive heart valve growth in normal adult conditions and promoting cell proliferation in the early stages of repair, as we reported previously. Supported by a grant from the Heart and Stroke Foundation of Ontario (grant NA6204).
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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 | 0.000 |
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".