The histopathological effects of intracavernosal mitomycin-C injection in a rat Peyronie's disease model
Bibliographic record
Abstract
INTRODUCTION: We aimed to evaluate whether or not mitomycin-C (MMC) has an antifibrotic effect on transforming growth factor-beta (TGF-β)-induced Peyronie's disease (PD) in a rat model. METHODS: Eighteen 12-week-old male Sprague-Dawley rats were divided into three groups: Group 1=TGF-β1 (n=7); Group 2=TGF-β1+MMC (n=7); and Group 3=Sham group (0.25 ml bovine serum albumin injected) (n=4). All groups were sacrificed on the sixth week of the procedure and their penises were excised. All penis specimens were evaluated semi-quantitatively and quantitatively with histochemical, immunohistochemistry, and image analysis. RESULTS: Both Group 1 and Group 2 had significantly higher fibrosis scores and lower elastic fibers in both outer surface of tunica albuginea (TA) and subsinusoidal area compared with Group 3. When compared with Group 1, the amount of collagen was significantly decreased in Group 2. Intracavernosal MMC injection (Group 2) ended up with lower elastic fibers when compared with Group 1. According to the quantitative analyses, when compared with Groups 1 and 3, lower dorsal, ventral, and trabecular thickening values were seen in Group 2. These parameters were only statistically significant when compared with Group 1, suggesting the antifibrotic effect of TGF-β1-induced fibrosis. Both Groups 1 and 2 showed lower decorin staining levels in subsinusoidal areas of tunica albuginea (SATA) and subsinusoidal areas of trabecular wall (SATW) when compared with Group 3. The statistically significant difference was only detected between Group 1 and Group 3. CONCLUSIONS: Our study demonstrates the antifibrotic effects of MMC on PD. Further clinical studies are necessary to make inferences regarding its clinical use.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".