The role of fractional carbon dioxide laser in modulating smooth muscle actin expression in Keloid and Hypertrophic scars
Bibliographic record
Abstract
Background Keloids (K) and hypertrophic scars (HTS) are challenging due to their disfiguring nature and tendency to recur. The immunohistochemical expression of α-smooth muscle actin (α-SMA), which reflects fibroblast activity, may be influenced by fractional carbon dioxide (fCO 2 ) laser therapy, potentially improving scar appearance, and texture. Objective To assess the effects of fCO 2 laser treatment on the histopathological and clinical characteristics of K and HTS, focusing on the expression of α-SMA as a marker of treatment efficacy. Patients and methods This interventional study was performed on 30 patients with either K or HTS. Participants underwent three sessions of fCO 2 laser treatment. Scar assessments were conducted using the Vancouver Scar Scale, and skin biopsies were taken pre and post-treatment for histopathological and immunohistochemical analysis of α-SMA expression. Results Significant improvements were noted post-treatment, with reductions in scar erythema, pain, and pruritus ( P <0.05). The mean size of lesions also showed a significant decrease ( P <0.05). Furthermore, α-SMA expression notably decreased, with a reduction in strongly positive cases from 80% pretreatment to 10% post-treatment and an increase in negative cases from 10 to 60%. Overall, patient satisfaction correlated positively with these improvements. Conclusion fCO 2 laser treatment significantly alters the clinical and histopathological profiles of K and HTS, reducing α-SMA expression and improving overall scar quality.
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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) | 0.001 | 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".