Wound healing in oral mucosa results in reduced scar formation as compared with skin: Evidence from the red Duroc pig model and humans
Bibliographic record
Abstract
Scar formation is a common, unwanted result of wound healing in skin, but the mechanisms that regulate it are still largely unknown. Interestingly, wound healing in the oral mucosa proceeds faster than in skin and clinical observations have suggested that mucosal wounds rarely scar. To test this concept, we created identical experimental wounds in the oral mucosa and skin in red Duroc pigs and compared wound healing and scar development over time. We also compared the pig oral mucosal wound healing to similar experimental wounds created in human subjects. The findings showed significantly reduced scar formation at both clinical and histological level in the pig oral mucosa as compared with skin 49 days after wounding. Additionally, the skin scars contained a significantly increased number of type I procollagen immunopositive cells and an increased fibronectin content, while the oral mucosal wounds demonstrated a prolonged accumulation of tenascin-C. Furthermore, the pig oral mucosal wounds showed similar molecular composition and clinical and histological scar scores to human oral mucosal wounds. Thus, the reduced scar formation in the pig oral mucosa provides a model to study the biological processes that regulate scarless wound healing to find novel approaches to prevent scar formation in skin.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".