Clinical and molecular effects on mature burn scars after treatment with a fractional CO<sub>2</sub> laser
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: There have been several case reports of improvement in the appearance of mature burn scars following treatment with fractional CO(2) lasers. However, the biochemical mechanisms responsible for these improvements have not been elucidated. MATERIALS AND METHODS: Ten patients with mature, full-thickness, hypertrophic burn scars received initial treatment with a fractional CO(2) laser. Clinical improvement was measured with Vancouver Scar Scale as well as Patient and Observer Scar Assessment Scale. Fresh tissue samples were obtained before the initial treatment and 48 hours after the first treatment for TaqMan Real-time RT-PCR analyses. Expressions of several scar-related biological markers, including types I and III procollagen, matrix metalloproteinase (MMP)-1, -13, transforming growth factor (TGF)-β1, β2, β3, and basic fibroblast growth factor (bFGF), as well as microRNA miR-17-92 cluster, were investigated. RESULTS: There were significant improvements in both observer and subject ratings in all scales. Both types I and III procollagen mRNA levels were dramatically down-regulated after treatment, but the ratio of types I/III procollagen mRNA was not different. The expression of MMP-1 was significantly up-regulated after treatment, while TGF-β2, -β3, and bFGF levels were significantly down-regulated. Expression of miR-18a and miR-19a were dramatically up-regulated (P < 0.05) after treatment. CONCLUSIONS: Our study indicated that fractional CO(2) resulted in clinical improvement of mature burn scar. Alteration of types I and III procollagen, MMP-1, TGF-β2, -β3, bFGF, as well as miRNAs miR-18a and miR-19a expression may be responsible for the clinical improvement after treatment. Our finding may have implications for novel treatments and further our understanding of fractional CO(2) laser treatment.
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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".