The mast cell stabilizer ketotifen prevents development of excessive skin wound contraction and fibrosis in red Duroc pigs
Bibliographic record
Abstract
Skin wound healing in Yorkshire pigs closely approximates human wound healing. Conversely, red Duroc pigs form fibroproliferative, hypercontractile scars. As mast cells have been implicated in several fibrotic conditions, the present study used these models to evaluate the potential role of mast cells in wound contraction and fibrosis. Immediately following the creation of full-thickness excisional wounds, the mast cell stabilizer ketotifen was used to treat both Yorkshire and red Durocs. Control red Durocs showed significantly more wound contraction than Yorkshires, both before and after reepithelialization. Ketotifen treatment significantly reduced the first phase of contraction in red Duroc wounds to a level equivalent to Yorkshire wounds, but had no detectable effect on the postepithelialization phase of contraction. Cessation of drug treatment after 10 weeks did not lead to resumption of excessive contraction in red Durocs, indicating that ketotifen blocked rather than delayed such contraction during a critical phase of healing. Ketotifen treatment also reduced the deposition of collagen within the red Duroc wounds, but did not affect Yorkshire wound contraction or collagen deposition. These results suggest that ketotifen may be an effective treatment for the reduction of excessive wound contraction and fibrosis in human cutaneous injuries, without affecting the normal healing process.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".