Nanofat Grafting in Burn Scar Rejuvenation
Bibliographic record
Abstract
Background: Burn scars often result in significant cosmetic and functional impairment, with current therapies offering limited efficacy and frequent complications. Nanofat grafting, enriched with adipose-derived stem cells, has emerged as a promising regenerative treatment to improve scar quality. Objective: To evaluate the clinical efficacy of autologous nanofat grafting in the rejuvenation of post-burn scars. Patients and Methods: This prospective case series included 20 patients with disfiguring burn scars treated at Mansoura University Hospital between April 2024 and April 2025. Patients underwent intradermal and subdermal nanofat injections, followed by clinical evaluation at baseline and three months post-treatment using the Vancouver Scar Scale (VSS). Statistical analysis was performed using SPSS version 22. Results: The mean VSS total score significantly improved from 8.90 ± 1.83 preoperatively to 6.60 ± 1.69 postoperatively (mean difference: 2.30 ± 1.26; p=0.001), representing a 25.8% reduction. Vascularity shifted favorably, with red scars decreasing from 50% to 35% and pink scars increasing from 15% to 55% (p=0.013). Pliability improved markedly, with yielding scars rising from 5% to 40% and firm scars declining from 70% to 30% (p=0.001). No significant correlations were found between scar improvement and patient age, sex, or scar surface area. Conclusion: Autologous nanofat grafting is a safe and effective modality for burn scar rejuvenation, leading to significant improvements in scar vascularity, pliability, and overall appearance. Long-term studies are warranted to confirm the durability of these outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".