<i>In Vivo</i> Study of the Inflammatory Modulating Effects of Low‐level Laser Therapy on iNOS Expression Using Bioluminescence Imaging
Bibliographic record
Abstract
This study was designed to demonstrate that bioluminescence imaging (BLI) can be used as a new tool to evaluate the effects of low-level laser therapy (LLLT) during in vivo inflammatory process. Here, the efficacy of LLLT in modulating inducible nitric oxide synthase (iNOS) expression using different therapeutic wavelengths was determined using transgenic animals with the luciferase gene under control of the iNOS gene expression. Thirty transgenic mice, FVB/N-Tg(iNOS-luc)Xen, were allocated randomly to one of four experimental groups treated with different wavelengths (lambda = 635, 785, 808 and 905 nm) or a control group (nontreated). Inflammation was induced by intra-articular injection of zymosan A in both knee joints. Laser treatment (25 mW cm(-2), 200 s, 5 J cm(-2)) was applied to the knees 15 min after inflammation induction. Measurements of iNOS expression were performed at various times (0, 3, 5, 7, 9 and 24 h) by measuring the bioluminescence signal using a highly sensitive charge-coupled device (CCD) camera. The results showed a significant increase in BLI signal after irradiation with 635 nm laser when compared to the nonirradiated animals and the other LLLT-treated groups, indicating wavelength dependence of LLLT effects on iNOS expression during the inflammatory process, and thus demonstrating an action spectrum of iNOS gene expression following LLLT in vivo that can be detected by BLI. Histological analysis was also performed and demonstrated the presence of fewer inflammatory cells in the synovial joints of mice irradiated with 635 nm compared with nonirradiated knee joints.
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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 | 0.001 |
| 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".