FGF‐2 Regulates Valve Interstitial Cell Repair Through TGF‐b/Smad Signaling
Bibliographic record
Abstract
Background Transforming growth factor‐β (TGF‐β) and fibroblast growth factor‐2 (FGF‐2) both promote valve interstitial cell (VIC) repair. The relationship between TGF‐β and FGF‐2 in wound repair is not understood. Methods VIC confluent monolayers were wounded and treated with FGF‐2, FGF‐2 neutralizing antibody, TGF‐β neutralizing antibody and/or betaglycan antibody. Phosphorylated‐Smad2/3 (pSmad2/3) was localized at the wound edge (WE). Down‐regulation of pSmad2/3 protein expression was achieved via siRNA transfection. Results FGF‐2 treatment increased the number of VICs with nuclear pSmad2/3 staining at the WE compared to nontreated WE. Treatment with TGF‐β neutralizing antibody alone or with FGF‐2 present resulted in a similar decrease in nuclear pSmad2/3. Treatment with FGF‐2 neutralizing antibody alone or with FGF‐2 present showed a similar decrease in nuclear pSmad2/3; however the effect of FGF‐2 neutralizing antibody was less than that of TGF‐β neutralizing antibody. Incubation with betaglycan antibody inhibited FGF‐2‐mediated pSmad2/3 signaling. Down‐regulation of pSmad2/3 reduced the extent to which FGF‐2 promoted wound closure. Conclusion FGF‐2 promotes in vitro VIC wound repair, at least in part, through the TGF‐β/Smad2/3 signaling pathway. Supported by the Heart and Stroke Foundation of Ontario (grant NA6204) and the Canadian Institutes for Health Research (grant 84228).
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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) | 0.002 | 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".