Effects of low intensity laser irradiation during healing of skin lesions in the rat
Bibliographic record
Abstract
OBJECTIVE: To determine whether laser light can improve healing of skin wounds by killing wound bacteria while simultaneously accelerating host tissue activity. MATERIALS AND METHODS: Wounds on the rat dorsum were irradiated or sham-irradiated three times weekly from days 1 to 19 using 635 or 808 nm diode lasers at 1 or 20 J/cm(2). Wound area and bacterial growth were evaluated three times weekly. Histological analysis was performed on days 8 and 19. Immunohistochemical analysis was performed on day 19. RESULTS: Wounds that were irradiated using 635 nm light at 1 J/cm(2) healed similarly to controls. Wounds that were irradiated using 808 nm (1 and 20 J/cm(2), P<or=0.05) and 635 nm light (20 J/cm(2), P<or=0.05) were worse in wound maximum area at day 3 compared with controls. Further, 635 nm light at 20 J/cm(2) delayed wound closure at day 19 (ASL = 0.0127). Bacterial colonization of wounds was altered using 635 nm (20 J/cm(2), P<0.0001) and 808 nm light (1 J/cm(2), P<0.0001; 20 J/cm(2), P = 0.02). In particular, the presence of normal skin flora decreased (P<0.0001-0.0002) and, when using 808 nm light, the presence of S. aureus increased (P = 0.0001). There was histological evidence of advanced repair using 635 nm at 1 J/cm(2) at day 8 (ASL<0.04). In contrast, markers of acute repair were increased and of late repair were decreased at day 19 using 635 nm at 20 J/cm(2) (ASL<0.04). CONCLUSIONS: This study demonstrates that while clearing wounds of certain bacteria is feasible it does not necessarily translate into a healing advantage. When normal flora are disturbed, environmental organisms more readily colonize the wound surface. It is not clear when using 808 nm light whether the loss of normal flora in the wound alone is responsible for the proliferation of S. aureus or whether the light adds to the effect by stimulating S. aureus growth.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".