Treatment of Keloid Scars with Botulinum Toxin Type A versus Triamcinolone in an Athymic Nude Mouse Model
Bibliographic record
Abstract
BACKGROUND: Keloid scarring is a serious condition that mostly affects patients of African or Asian descent. Often disfiguring, this condition can have devastating psychosocial consequences. To date, no treatment modality has been proven ideal. The authors' objectives were (1) to determine the efficacy of botulin toxin type A injection for the treatment of keloid scars compared to steroid injection and to control saline injection (this was achieved through a basic science animal model using athymic nude mice and implanted human keloid tissue); and (2) to analyze the histopathologic changes that occur in an organized keloid scar following botulinum toxin type A injection as compared to steroid and saline injections. METHODS: Keloid scars from four patients were excised and implanted subcutaneously into 28 mice. Three small keloid tissue samples were implanted in each of the 28 mice. One week after implantation, each implant received one of three injections: botulinum toxin type A (treatment drug), saline (control), or steroid injection (first-line gold standard). The keloid tissue was extracted 3 weeks after implantation. Weight analysis, immunohistochemistry, and standard hematoxylin and eosin pathologic analysis were performed on each extracted tissue sample. RESULTS: Paired t test analysis of pretreatment and posttreatment tissue weights revealed a statistically significant difference between the treatment and control groups (p < 0.05). Analysis by a blinded pathologist confirmed fewer collagen bundles in the treatment group. Immunohistochemistry with Ki-67, a marker of cell proliferation, revealed significantly less staining in the treatment groups. CONCLUSION: Botulinum toxin type A could be an effective treatment for keloid 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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".