Cytokine Profiling and Intra-Articular Injection of Autologous Platelet-Rich Plasma in Knee Osteoarthritis
Bibliographic record
Abstract
Osteoarthritis (OA) is a degenerative joint disease leading to joint pain and stiffness. Due to lack of effective treatments, physical and psychological disabilities caused by OA have a detrimental impact on the patient’s quality of life. Emerging evidence suggests that intra-articular injection of platelet-rich plasma (PRP) may provide favorable results since PRP comprises not only a high level of platelets but also a huge amount of cytokines, chemokines, and growth factors. However, the precise mechanism and standardization method remain uncertain. This study aimed to examine cytokine profiling in both PRP and platelet-poor plasma (PPP) of knee OA patients and to determine the effects of PRP on OA chondrocytes and knee OA patients. PRP contained a wide variety of cytokines, chemokines, growth factors, and autologous intra-articular PRP injection resulted in favorable outcomes in knee OA patients. Significant increases in levels of IL-1, IL-2, IL-7, IL-8, IL-9, IL-12, TNF-α, IL-17, PDGF-BB, bFGF, and MIP-1β were detected in PRP compared to PPP (p < 0.001). An in vitro study showed a marked increase in proliferation in OA chondrocytes cultured with PRP, compared to PPP and fetal bovine serum (p < 0.001). In a clinical study, knee OA patients treated with PRP showed improvement of physical function and pain, assessed by physical performance, Western Ontario and McMaster Universities Arthritis Index and visual analog scale. Our findings from both in vitro and clinical studies suggest that intra-articular PRP injection in knee OA patients may be a potential therapeutic strategy for alleviating knee pain and delaying the need for surgery.
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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) | 0.000 | 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 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".