Intra-Articular Exosome and Intraarticular Laser Therapy for Osteoarthritis; Preliminary Non-Surgical Approach
Bibliographic record
Abstract
Background: Stem cells and exosomes offer a non-surgical approach to treating osteoarthritis (OA). Exosomes play an essential role in intercellular communication that has been widely studied. Furthermore, multi-wavelength intra-articular laser therapy is a newly developed technique in the treatment of various diseases. Here, we investigated the therapeutic potential and mechanism of adipose tissue-derived exosomes to alleviate knee osteoarthritis compared with exosome therapy combined with intra-articular laser therapy. Method: Adipose-derived exosomes were isolated and purified. Five volunteers received intra-articular injection of autologous exosomes in the right knee, while the left knee of these volunteers received intra-articular exosome injection and intra-articular infrared laser, 808 nm. To avoid any bias in the patient, the laser fiber was passed through the inserted needle for exosome injection, but without radiation, and on the right knee, an invisible infrared laser was applied. Visual analog scales (VAS) and Western Ontario and McMaster Universities Arthritis Index (WOMAC) were collected the week before the procedure, and every month until the sixth months after injection for both knees. Discussion: Both intra-articular exosome injections improved pain and functional outcomes at 6-month follow-up in volunteers with knee osteoarthritis. The differences between exosome injection and exosomes combined with intra-articular laser therapy are in terms of clinical improvement rates and functional outcome rates. Conclusion: Combination of stemcell or exosome therapy with intra-articular laser therapy could be considered as a feasible option in non-surgical treatment approaches. It could be also used as a preventive purpose.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".