P31. CONTROL OF THE MINERALOCORTICOID RECEPTOR ENHANCES WOUND HEALING AND MITIGATE FIBROSIS AFTER THERMAL INJURY
Bibliographic record
Abstract
PURPOSE: Adhesion, contracture, and the formation of disfiguring, hypertrophic scars remain some of the most recalcitrant problems in care of the burned patient. The central pathology driving fibrosis is an imbalance of extracellular matrix (ECM). The mineralocorticoid receptor (MR) is a nuclear receptor with shared ligand-activity from both its primary activator, aldosterone, and glucocorticoids and has cell specific effects on proliferation, migration, and ECM production in fibroblasts and keratinocytes. Here we demonstrate that MR-inhibition with FDA approved spironolactone enhances epithelialization healing and mitigates hypertrophic collagen deposition after burns in mice. METHODS: Female athymic mice sustained bilateral 1 cm full-thickness thermal injury and were stratified into either a) vehicle, b) spironolactone, c) aldosterone, or d) aldosterone + spironolactone. Aldosterone applied via subcutaneous pump (Alzet) spironolactone delivered intraperitoneally. Mice followed photographically for 4-to-6-weeks. At sacrifice wound biopsies were collected for H&E, Trichrome, and protein. RESULTS: Histologic evaluation of healing wounds from burned mice demonstrated persistence of inflammation, wound edema, and immature ECM. In mice treated with spironolactone this effect if alleviated by 4-weeks post-injury concurrent with gross findings of rapid wound epithelialization. Spironolactone treatment additionally enhanced gross and histologic evidence of scar resolution and resulted in a decreased in collagen staining vs. controls. CONCLUSION: We found a significant early improvement in wound epithelialization with spironolactone therapy. This suggests a potentially distinct effect from the proposed ECM-modifying strategy hypothesized. More analysis is needed but this data supports that MR-inhibition may be a valuable new therapeutic in treatment of burn scars.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".