Identification of microRNA-181a-5p and microRNA-4454 as mediators of facet cartilage degeneration
Bibliographic record
Abstract
Osteoarthritis (OA) of spine (facet joints [FJs]) is one of the major causes of severe low back pain and disability worldwide. The degeneration of facet cartilage is a hallmark of FJ OA. However, endogenous mechanisms that initiate degeneration of facet cartilage are unknown, and there are no disease-modifying therapies to stop FJ OA. In this study, we have identified microRNAs (small noncoding RNAs) as mediators of FJ cartilage degeneration. We first established a cohort of patients with varying degrees of facet cartilage degeneration (control group: normal or mild facet cartilage degeneration; FJ OA group: moderate to severe facet cartilage degeneration) and then screened 2,100 miRNAs and identified 2 miRNAs (miR-181a-5p and miR-4454) that were significantly elevated in FJ OA cartilage compared with control facet cartilage. We further explored their role, function, and signaling mechanisms using computational, in vitro functional, and in vivo studies. We specifically indicate that miR-181a-5p and miR-4454 are involved in promoting inflammatory, catabolic, and cell death activity in FJ chondrocytes. This is the first report to our knowledge that identifies miR-181a-5p and miR-4454 as mediators of cartilage degeneration in FJs and potential therapeutic targets for stopping cartilage degeneration.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".