Fluorescent light energy modulates healing in skin grafted mouse model
Bibliographic record
Abstract
Skin grafting is often the only treatment for skin trauma when large areas of tissue are affected. This surgical intervention damages the deeper dermal layers of the skin with implications for wound healing and a risk of scar development. Photobiomodulation (PBM) therapy modulates biological processes in different tissues, with a positive effect on many cell types and pathways essential for wound healing. This study investigated the effect of fluorescent light energy (FLE) therapy, a novel type of PBM, on healing after skin grafting in a dermal fibrotic mouse model. Split-thickness human skin grafts were transplanted onto full-thickness excisional wounds on nude mice. Treated wounds were monitored, and excised xenografts were examined to assess healing and pathophysiological processes essential for developing chronic wounds or scarring. Results demonstrated that FLE treatment initially accelerated re-epithelialization and rete ridge formation, while later reduced neovascularization, collagen deposition, myofibroblast and mast cell accumulation, and connective tissue growth factor expression. While there was no visible difference in gross morphology, we found that FLE treatment promoted a balanced collagen remodeling. Collectively, these findings suggest that FLE has a conceivable effect at balancing healing after skin grafting, which reduces the risk of infections, chronic wound development, and fibrotic scarring.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".