The Contribution of Peroxisome Proliferator–Activated Receptor Gamma to Cutaneous Wound Healing
Bibliographic record
Abstract
Significance: Cutaneous tissue repair involves an initial inflammatory phase, followed by a fibroproliferative phase and finally by a resolution phase. Failure to initiate fibroblast recruitment during the fibroproliferative phase results in chronic wounds, whereas failure to terminate the fibroproliferative phase results in fibroproliferative disorders. Thus, understanding how to regulate the fibroproliferative phase of tissue repair is, therefore, of high clinical relevance. Controlling the rate of the fibroproliferative response is essential to promote proper wound repair. Recent Advances: (1) The myofibroblast is essential for mediating the fibroproliferative phase of tissue repair. (2) The potent profibrotic cytokine transforming growth factor beta (TGF-β) is a major in vivo contributor to myofibroblast differentiation and activity in vivo. Critical Issues: An increasing body of evidence indicates that the transcription factor peroxisome proliferator–activated receptor gamma (PPAR-γ) plays a key in vivo role in suppressing the fibrogenic response by antagonizing TGF-β signaling. Excessive scarring and/or chronic wounds, caused by a dysregulated fibroproliferative phase, are major clinical problems in response to tissue injury. Future Directions: The development of drugs to control the rate of the fibroproliferative response are clinically relevant. Controlling PPAR-γ activity may be useful for prevention of scarring as well as for promoting the closure of chronic wounds.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".