Dynamics of Transforming Growth Factor Beta Signaling in Wound Healing and Scarring
Bibliographic record
Abstract
SIGNIFICANCE: Wound healing is an intricate biological process in which the skin, or any other tissue, repairs itself after injury. Normal wound healing relies on the appropriate levels of cytokines and growth factors to ensure that cellular responses are mediated in a coordinated manner. Among the many growth factors studied in the context of wound healing, transforming growth factor beta (TGF-β) is thought to have the broadest spectrum of effects. RECENT ADVANCES: Many of the molecular mechanisms underlying the TGF-β/Smad signaling pathway have been elucidated, and the role of TGF-β in wound healing has been well characterized. Targeting the TGF-β signaling pathway using therapeutic agents to improve wound healing and/or reduce scarring has been successful in pre-clinical studies. CRITICAL ISSUES: through mechanisms that have not been fully elucidated. The challenge of translating preclinical studies targeting the TGF-β signaling pathway to a clinical setting may require more extensive preclinical research using animal models that more closely mimic wound healing and scarring in humans, and taking into account the spatial, temporal, and cell-type-specific aspects of TGF-β isoform expression and function. FUTURE DIRECTIONS: Understanding the differences in TGF-β isoform signaling at the molecular level and identification of novel components of the TGF-β signaling pathway that critically regulate wound healing may lead to the discovery of potential therapeutic targets for treatment of impaired wound healing and pathological scarring.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".