Transforming Growth Factor Beta Signaling in Cutaneous Wound Healing: Lessons Learned from Animal Studies
Bibliographic record
Abstract
SIGNIFICANCE: Wound healing is a complex physiological process involving a multitude of growth factors, among which transforming growth factor beta (TGF-β) has the broadest spectrum of effects. Animal studies have provided key information on the mechanisms of TGF-β action in wound healing and have guided the development of therapeutic strategies targeting the TGF-β pathway to improve wound healing and scarring outcome. RECENT ADVANCES: Development of tissue-specific expression systems for overexpression or knockout of TGF-β signaling pathway components has led to novel insight into the role of TGF-β signaling in wound healing. This work has also identified molecules that might serve as molecular targets for the treatment of pathological skin conditions such as chronic wounds and excessive scarring (fibrosis). CRITICAL ISSUES: Many of the mouse models with genetic alterations in the TGF-β signaling pathway develop an underlying skin abnormality, which may pose some limitations on the interpretation of wound-healing results obtained in these animals. Also, TGF-β's pleiotropic effects on many cell types throughout all phases of wound healing present a challenge in designing specific strategies for targeting the TGF-β signaling pathway to promote wound healing or reduce scarring. FUTURE DIRECTIONS: during wound healing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".