Ketotifen directly modifies the fibrotic response of human skin fibroblasts
Bibliographic record
Abstract
Fibrosis is a destructive, end-stage disease process. In the skin, it is associated with systemic sclerosis and scarring with considerable health burden. Ketotifen is a clinical antihistamine and mast cell stabilizer. Studies have demonstrated mast cell-dependent anti-fibrotic effects of ketotifen but direct effects on fibroblasts have not been determined. Human dermal fibroblasts were treated with pro-fibrotic transforming growth factor-β1 (TGFβ) followed by ketotifen or control treatments to determine direct effects on fibrotic fibroblasts. Ketotifen impaired TGFβ-induced α-smooth muscle actin gene and protein responses and decreased cytoskeletal- and contractility-associated gene responses associated with fibrosis. Ketotifen reduced Yes-associated protein phosphorylation, transcriptional coactivator with PDZ binding motif transcript and protein levels, and phosphorylation of protein kinase B. In a fibroblast-populated collagen gel contraction assay, ketotifen reduced the contractile activity of TGFβ-activated fibroblasts. In a murine model of bleomycin-induced skin fibrosis, collagen density and dermal thickness were significantly decreased in ketotifen-treated mice supporting in vitro findings. These results support a novel, direct anti-fibrotic activity of ketotifen, reducing pro-fibrotic phenotypic changes in fibroblasts and reducing collagen fibres in fibrotic mouse skin. Together, these findings suggest novel therapeutic potential and a novel mechanism of action for ketotifen in the context of fibrosis.
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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".