Angiotensin II Type I Receptor Blockade Is Associated with Decreased Cutaneous Scar Formation in a Rat Model
Bibliographic record
Abstract
BACKGROUND: Angiotensin II engagement of angiotensin II type 1 receptor (AT1R) is implicated in fibrogenesis, with AT1R blockers used clinically to attenuate cardiac and renal fibrosis. The authors tested the hypothesis that the AT1R blocker losartan could reduce postsurgical cutaneous scarring in rats. METHODS: Human dermal fibroblasts were treated with losartan and assessed for viability, contractile activity, migration, and profibrotic gene transcription by means of calcein, collagen gel, scratch, and quantitative reverse transcriptase polymerase chain reaction assays, respectively. Monocyte migration and adhesion to losartan-treated and control fibroblasts were examined. Losartan effects in vivo were assessed using a mechanical distraction hypertrophic scar model. Three days after incisions were made and closed on their backs, rats were assigned randomly to receive drinking water with or without losartan (1 mg/kg per day; n = 6 per group). Distraction devices were applied and activated up to day 14. On day 28, scars underwent cross-sectional area and elevation index analyses, and α-SMA (alpha-smooth muscle actin) and CD68 (monocyte/macrophage marker) immunostaining. RESULTS: Losartan-treated human dermal fibroblasts displayed decreased contractile activity, migration, and gene expression of transforming growth factor-β1, collagen I, and monocyte chemoattractant protein-1 relative to controls (p < 0.05). Monocyte migration and adhesion to losartan-treated fibroblasts were reduced (p < 0.01). Compared to controls, scars from losartan-treated rats demonstrated decreased cross-sectional area (19.4 ± 3.1 mm versus 45.0 ± 5.2 mm; p = 0.002), elevation index (1.5 ± 0.1 versus 2.6 ± 0.3; p = 0.003), and α-SMA and CD68 immunostaining (p < 0.001). CONCLUSIONS: Losartan decreases myofibroblast activity and reduces monocyte trafficking to cutaneous scar. These findings support losartan as a potential novel therapy for the prevention of hypertrophic 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) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".