Enhancing Wound Healing in Aesthetic Surgery: The Role of Chitosan–Moringa oleifera Gel
Bibliographic record
Abstract
Background & Objectives: This study aimed to evaluate the efficacy of chitosan gel infused with the hydroalcoholic extract of Moringa oleifera leaf powder in promoting wound healing and reducing scar formation following cosmetic breast surgery. Materials & Methods: A total of 42 patients undergoing cosmetic breast surgery were enrolled in this randomized controlled study. Each patient’s breast was divided into two lateral hemispheres: one side received Moringa oleifera gel, while the other served as a control with a placebo gel. The gels were applied for two weeks postoperatively. Scar appearance was assessed at two weeks and three months post-surgery using the Vancouver Scar Scale (VSS), which evaluates vascularity, pigmentation, elasticity, and height. Data were analyzed to compare outcomes between the treated and control sites. Results: Of the initial 42 patients, 36 completed the study. Twenty-five and thirty-one patients were evaluated at the two-week and three-month follow-ups, respectively. Wounds treated with the Moringa-infused gel demonstrated an 84% improvement in healing compared to those treated with the placebo. Statistically significant improvements were observed in all evaluated parameters—including vascularity, pigmentation, height, and elasticity—in favor of the Moringa oleifera gel. Conclusions: Chitosan gel infused with the hydroalcoholic extract of Moringa oleifera leaf powder significantly enhances wound healing and improves scar appearance following cosmetic breast surgery. These findings highlight its potential as a valuable adjunct in postoperative wound care for reducing scarring and promoting tissue regeneration.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".