The Role of Local Inflammation and Hypoxia in the Formation of Hypertrophic Scars—A New Model in the Duroc Pig
Bibliographic record
Abstract
Hypertrophic scars continue to be a major burden, especially after burns. Persistent inflammation during wound healing appears to be the precipitating aspect in pathologic scarring. The lack of a standardized model hinders research from fully elucidating pathophysiology and therapy, as most therapeutic approaches have sparse evidence. The goal of this project was to investigate the mechanisms of scar formation after prolonged wound inflammation and to introduce a method for generating standardized hypertrophic scars by inducing prolonged inflammation. Four wound types were created in Duroc pigs: full-thickness wounds, burn wounds, and both of them with induced hyperinflammation by resiquimod. Clinical assessment (Vancouver Scar Scale), tissue oxygenation by hyperspectral imaging, histologic assessment, and gene expression analysis were performed at various time points during the following five months. Native burn wounds as well as resiquimod-induced full-thickness and burn wounds resulted in more hypertrophic scars than full-thickness wounds. The scar scale showed significantly higher scores in burn- and resiquimod-induced wounds compared with full-thickness wounds as of day 77. These three wound types also showed relative hypoxia compared with uninduced full-thickness wounds in hyperspectral imaging and increased expression of HIF1a levels. The highest number of inflammatory cells was detected in resiquimod-induced full-thickness wounds with histologic features of hypertrophic scars in burn and resiquimod-induced wounds. Gene expression analysis revealed increased inflammation with only moderately altered fibrosis markers. We successfully created hypertrophic scars in the Duroc pig by using different wound etiologies. Inflammation caused by burns or resiquimod induction led to scars similar to human hypertrophic scars. This model may allow for the further investigation of the exact mechanisms of pathological scars, the role of hypoxia and inflammation, and the testing of therapeutic approaches.
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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) | 0.000 | 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) | 0.001 | 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".