AT2R Activation Improves Wound Healing in a Preclinical Mouse Model
Bibliographic record
Abstract
Abnormal skin healing resulting in chronic wounds or hypertrophic scarring remains a major healthcare burden. Here, the antifibrotic angiotensin II type 2 receptor (AT2R) signaling pathway was modulated to determine its impact on cutaneous wound healing. Balb/c mice received two splinted full-thickness wounds. Topical treatments with the selective AT2R agonist compound 21 (C21) and/or selective antagonist PD123319 or saline vehicle were administered until sacrifice on post-wounding days 7 or 10. The rate of wound re-epithelialization was accelerated by PD123319 and combination treatments. In vitro, C21 significantly reduced human fibroblast migration. C21 increased both collagen and vascular densities at days 7 and 10 post-wounding and collagen I:III ratio at day 10, while PD123319 and combination treatments decreased them. Genes associated with regeneration and repair were upregulated by C21, while PD123319 treatment increased the expression of genes associated with inflammation and immune cell chemotaxis. C21 treatment reduced wound total leukocyte and neutrophil staining densities, while PD123319 increased these and macrophage densities. Overall, AT2R activation with C21 yields wounds that mature more quickly with structural, cellular, and gene expression profiles more closely approximating unwounded skin. These findings support AT2R signal modulation as a potential therapeutic target to improve skin quality during wound healing.
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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 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".