Identification of microRNA-181a-5p and microRNA-4454 as mediators of facet cartilage degeneration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it