Lipofilling—A Regenerative Alternate for Remodeling Burn Scars: A Clinico-Immunohistopathological Study
Bibliographic record
Abstract
Abstract Introduction Any injury involving the dermis will lead to scarring. Scar tissue can cause functional limitations, cosmetic impairments, pain, and itch. Adipose-derived stem cells have also been shown to play a role in scar modulation. This study evaluates changes in lipofilled scar over the period of time and compares it with non-lipofilled scar tissue. Materials and Methods A prospective case–control study with intraindividual follow-up was performed on 30 adult patients with post-burn scars from November 2016 to May 2019. Clinical, histopathological, and immunohistochemical parameters were assessed among the case and control regions of the scar. Results Mean age of the study population was 30.6 years. The duration of the scar included in this study ranged from 1 to 28 years, with a mean duration of 5.91 years. There was a significant reduction in pain, itch, stiffness, and an increase in the pliability of the scar, and a substantial improvement in the modified Vancouver Scar Score in the lipofilled group. In histopathological analysis, the case group showed organized parallel collagen fibers, a significant reduction in melanocytes, improvement in vascularity, and a significantly increased amount of collagen fibers at the reticular dermis. Immunohistochemical analysis indicated new cell synthesis in the scar tissue and reduced melanocytes. Conclusion The remodeling effect of adipocyte-derived stem cells is long-lasting, and there is a gradual improvement in most of the parameters. Lipofilling has regenerative capacity, which leads to the improved overall appearance of scar and improvement at the cellular level.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".