Mechanical Micronization of Lipoaspirates Combined with Fractional CO2 Laser for the Treatment of Hypertrophic Scars
Bibliographic record
Abstract
BACKGROUND: Treating hypertrophic scars remains challenging. Stromal vascular fraction (SVF) gel is produced by a purely mechanical process from lipoaspirates, rich in adipose-derived stem cells, and has showed therapeutic potential on scars. However, controversial effects on hypertrophic scars are emerging. This study aimed to assess the therapeutic effects of SVF gel combined with fractional CO 2 laser on hypertrophic scars. METHODS: A rabbit ear hypertrophic scar model was established. SVF gel combined with fractional CO 2 laser was conducted for hypertrophic scars in rabbits. Scar alleviation in rabbits was observed based on the appearance and histology of scars, and the underlying mechanism was investigated by tissue immunologic analyses and quantitative real time polymerase chain reaction. At last, six patients with hypertrophic scar were treated by SVF gel combined with fractional CO 2 laser. Therapeutic effects were assessed using the Vancouver Scar Scale. RESULTS: Following the treatments, hypertrophic scars became less apparent and softer, the dermis became thinner, and collagen fibers appeared looser and arranged in a more organized pattern. The SVF gel plus fractional CO 2 laser group showed the most obvious improvement. In addition, SVF gel combined with fractional CO 2 laser increased adipogenesis in scar tissue, and adipose tissue regeneration was observed. Hypertrophic scars in patients were alleviated after treatment with SVF gel combined with fractional CO 2 laser. CONCLUSIONS: SVF gel transplantation combined with fractional CO 2 laser showed encouraging therapeutic effects on hypertrophic scars. Although further investigation is necessary, this technique has great potential for clinical application to treat hypertrophic scars. CLINICAL RELEVANCE STATEMENT: This is a new technique for treating hypertrophic scars.
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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 | 0.000 | 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) | 0.000 | 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".