Effects of photobiomodulation and a physical exercise program on the expression of inflammatory and cartilage degradation biomarkers and functional capacity in women with knee osteoarthritis: a randomized blinded study
Bibliographic record
Abstract
BACKGROUND: The knee osteoarthritis (OA) is a joint disease characterized by degradation of articular cartilage that leads to chronic inflammation. Exercise programs and photobiomodulation (PBM) are capable of modulating the inflammatory process of minimizing functional disability related to knee OA. However, their association on the concentration of biomarkers related to OA development has not been studied yet. The aim of the present study is to investigate the effects of PBM (via cluster) with a physical exercise program in functional capacity, serum inflammatory and cartilage degradation biomarkers in patients with knee OA. METHODS: Forty-two patients were randomly allocated in 3 groups: ESP: exercise + sham PBM; EAP: exercise + PBM and CG: control group. Six patients were excluded before finished the experimental period. The analyzed outcomes in baseline and 8-week were: the Western Ontario and McMaster Universities Osteoarthritis (WOMAC) and the evaluation of serum biomarkers concentration (IL-1β, IL-6, IL-8, IL-10 e TNF-α, and CTX-II). RESULTS: An increase in the functional capacity was observed in the WOMAC total score for both treated groups (p < 0.001) and ESP presents a lower value compared to CG (p < 0.05) the 8-week post-treatment. In addition, there was a significant increase in IL-10 concentration of EAP (p < 0.05) and higher value compared to CG (p < 0.001) the 8-week post-treatment. Moreover, an increase in IL-1β concentration was observed for CG (p < 0.05). No other difference was observed comparing the other groups. CONCLUSION: Our data suggest that the physical exercise therapy could be a strategy for increasing functional capacity and in association with PBM for increasing IL-10 levels in OA knee individuals. TRIAL REGISTRATION: ReBEC (RBR-7t6nzr).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".