Enhanced Scar Reduction With Triamcinolone Acetonide and Botulinum Toxin A Combination Therapy Compared to Botulinum Toxin A Monotherapy: A Translational Pilot Study
Bibliographic record
Abstract
BACKGROUND: The efficacy of combining triamcinolone acetonide (TAC) and botulinum toxin type A (BTX-A) to treat hypertrophic scars and keloids remains unclear. OBJECTIVES: This pilot study aimed to compare the clinical efficacy of BTX-A monotherapy and TAC + BTX-A combination therapy in improving scar conditions and to investigate potential mechanisms with a rat burn scar model. METHODS: This study was conducted between March 2022 and December 2024. Each patient's scar was randomly divided into 2 equal parts, with 1 part receiving TAC + BTX-A and the other BTX-A alone for 3 sessions at 4-week intervals. Scar thickness was measured with calipers, and the Vancouver Scar Scale (VSS) was administered to assess scar condition. Pain and pruritus were evaluated with a numeric rating scale (NRS) and visual analog scale (VAS), respectively, before each intervention (V1, V2, V3) and 4 weeks after the last session (V4). In the animal study, rats with burn scars were assigned to 5 groups: sham, untreated burn, TAC, BTX-A, and TAC + BTX-A. Scar thickness and expressions of alpha smooth muscle actin (α-SMA), transforming growth factor beta (TGF-β), connective tissue growth factor (CTGF), collagen I/III, laminin, and fibronectin were analyzed histologically. RESULTS: Clinically, both therapies reduced scar thickness, VSS, NRS, and VAS scores. However, combination therapy showed superior reductions in scar thickness (V2-V4) and VSS scores (V3, V4) compared to BTX-A monotherapy. No significant differences were observed in NRS and VAS scores. The animal study confirmed that combination therapy more effectively reduced scar thickness, TGF-β and CTGF levels, and the collagen I/III ratio, and increased the laminin/fibronectin ratio. CONCLUSIONS: TAC + BTX-A combination therapy enhances scar reduction by more effectively suppressing fibroblast proliferation and activation through TGF-β and CTGF downregulation, promoting scar remodeling.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".