MC1R reduces scarring and rescues stalled healing in a preclinical chronic wound model
Bibliographic record
Abstract
Abstract Cutaneous healing results in scarring with significant functional and psychological sequelae, while chronic non-healing wounds represent repair failure often with devastating consequences, including amputation and death. Due to a lack of effective therapies, novel interventions addressing scarring and chronic wounds are urgently needed. Here, we demonstrate that harnessing melanocortin 1 receptor with a selective agonist (MC1R-Ag) confers multifaceted benefits to wound repair. MC1R-Ag accelerates wound closure and re-epithelialization while improving wound bed perfusion and lymphatic drainage by promoting angiogenesis and lymphangiogenesis. Concomitant reductions in oxidative stress, inflammation and scarring were also observed. To evaluate the therapeutic potential of targeting MC1R in pathological healing, we established a novel murine model that recapitulates the hallmarks of human non-healing wounds. This model combines advanced age and locally elevated oxidative stress. Remarkably, topical application of MC1R-Ag restored repair, whereas disrupting MC1R signalling exacerbated the chronic wound phenotype. Our study highlights MC1R agonism as a promising therapeutic approach for scarring and non-healing wound pathologies, and our chronic wound model as a valuable tool for elucidating ulcer development mechanisms.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".