Focal Adhesion Kinase: A Key Mediator of Transforming Growth Factor Beta Signaling in Fibroblasts
Bibliographic record
Abstract
Significance: There is no effective drug therapy for scarring and fibrotic disease. The cytokine transforming growth factor beta (TGF-β) promotes tissue repair, but its excessive action can lead to over exuberant scarring and fibrotic disease. However, owing to the multifunctional nature of TGF-β, broad targeting of the canonical Smad–TGF-β signaling pathway in vivo is likely to have unintended, deleterious consequences. Recent Advances: (1) The myofibroblast is the essential cell type that mediates tissue repair and fibrosis. (2) TGF-β is an essential contributor to myofibroblast differentiation and activity. (3) TGF-β selectively promotes tissue repair and fibrosis via the noncanonical focal adhesion kinase (FAK) pathway; FAK mediates myofibroblast differentiation, and hence may represent a novel intervention point for drugs treating fibrotic disease. Critical Issues: Excessive scarring (e.g., in hypertrophic scars, keloids, and scleroderma) is characterized by enhanced TGF-β signaling and is a major clinical problem. Drugs that selectively and effectively control the profibrotic action of TGF-β is therefore of clinical relevance. Future Directions: FAK inhibition may represent a novel therapy for scarring disorders.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".