The intra‐articular injection of adipose‐derived stem cells decreases pain and reduces inflammation in knee osteoarthritis, with or without the addition of platelet‐rich plasma also improves functionality
Bibliographic record
Abstract
The increase of individuals with Osteoarthritis (OA) has generated an increase in public spending in the treatments, which are still not that effective. So, the purpose of this study was to analyze and compare four types of interventions: platelet-rich plasma (PRP), adipose-derived stem cells (ADSCs), ADSCs + PRP and the standard surgical video arthroscopy (All groups passed through standard arthroscopy before intervention). The evaluation was performed by applying the questionnaires Western Ontario McMaster Universities, Short Form Health Survey 36 and Visual Analog Pain Scale, also by analyzing the synovial fluid (inflammatory cytokines, enzymatic, colorimetric and viscosity analysis), this evaluation happened in two moments: before the surgical procedures and after 6 months of the interventions and also was made a comparison to standard arthroscopy. The questionnaires results showed a greater improvement in the scores of the domains analyzed in the ADSCs + PRP group, followed by the ADSCs and PRP group. In the evaluation of inflammatory cytokines, there was a significant reduction in the cytokine IL-1b only in the ADSCs + PRP group (46%) and ADSCs (31%), of IL-6 in the ADSCs + PRP group (72%), of IL-8 in the ADSCs + PRP group (50%) and ADSCs (31%), and TNF in the ADSCs + PRP group (46%). There was also a significant increase in the amount of total proteins (79%) in the control group and polymorphonuclear cells (47%) in the ADSCs + PRP group. Taking all the results into account, we infer that therapies with ADSCs + PRP and only ADSCs are safe and effective over 6 months for the improvement of pain, functional capacity and joint inflammation in volunteers with OA. It is also considered that the use of ADSCs + PRP, particularly, is a promising alternative to help manage this disease, due to the better results presented among the four propose interventions.
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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 | 0.000 |
| 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".