Characterization of microRNA Levels in Synovial Fluid from Knee Osteoarthritis and Anterior Cruciate Ligament Tears
Bibliographic record
Abstract
This study investigated modifications of microRNA expression profiles in knee synovial fluid of patients with osteoarthritis (OA) and rupture of the anterior cruciate ligament (ACL). Twelve microRNAs (26a-5p, 27a-3p, let7a-5p, 140-5p, 146-5p, 155-5p, 16-5p,186-5p, 199a-3p, 210-3p, 205-5p, and 30b-5p) were measured by real-time quantitative polymerase chain reaction (RT-qPCR) in synovial fluids obtained from 30 patients with ACL tear and 18 patients with knee OA. These 12 miRNAs were chosen on the basis of their involvement in pathological processes of bone and cartilage. Our results show that miR-26a-5p, miR-186-5p, and miR-30b-5p were expressed in the majority of OA and ACL tear samples, whereas miR-199a-3p, miR-210-3p, and miR-205-5p were detectable only in a few samples. Interestingly, miR-140-5p was expressed in only one sample of thirty in the ACL tear group. miR-140-5p has been proposed to modulate two genes (BGN and COL5A1100) that are involved in ligamentous homeostasis; their altered expression could be linked with ACL rupture susceptibility. The expression of miR-30b-5p was higher in OA and chronic ACL groups compared to acute ACL samples. We provide evidence that specific miRNAs could be detected not only in synovial fluid of patients with OA, but also in post-traumatic ACL tears.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".