Wound Repair by Activated Heart Valve Interstitial Cells is Regulated by Transforming Growth Factor‐β
Bibliographic record
Abstract
Valve interstitial cells (VICs) undergo phenotypic changes in response to valve injury and disease, expressing α‐smooth muscle actin (α‐SMA), a marker of activation. We tested the hypothesis that TGF‐β is an important regulator of VIC activation and repair. We created in vitro experimental wounds by mechanical denudation of a confluent monolayer cultured in M199 containing 10% fetal bovine serum 2% penicillin/streptomycin/amphotericin. In TGF‐β neutralizing antibody non‐treated and treated wounds, we characterized VIC activation, TGF‐β expression and apoptosis by α‐SMA, TGF‐β, activated caspase‐3 and annexin V immunofluorescence respectively. We also quantified proliferation by BrdU labeling and repair by measuring wound closure. VICs at the wound edge are activated showing prominent α‐SMA staining which decreases over 24–96 hours post‐wounding. Upon wound closure, α‐SMA staining becomes similar to that of a quiescent nonwounded monolayer. TGF‐β and pSmad2/3 staining are more prominent and apoptosis is observed more frequently at the wound edge at 24 hours post‐wounding compared to the nonwounded monolayer. Addition of TGF‐β neutralizing antibody decreases VIC activation, proliferation and wound closure in a dose‐dependent manner. In conclusion, wounding activates VICs which upregulate TGF‐β. TGF‐β maintains VIC activation and in turn regulates proliferation and wound repair.
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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".