Melatonin Prevents Osteoarthritis-Induced Cartilage Degradation via Targeting MicroRNA-140
Bibliographic record
Abstract
Osteoarthritis (OA) is characterized by the progressive destruction of articular cartilage, which is involved in the imbalance between extracellular matrix (ECM) synthesis and degradation. MicroRNA-140-5p (miR-140) is specifically expressed in cartilage and plays an important role in OA-induced matrix degradation. The aim of this study was to investigate (1) whether intra-articular injection of melatonin could ameliorate surgically induced OA in mice and (2) whether melatonin could regulate matrix-degrading enzymes at the posttranscriptional level by targeting miR-140. In an in vitro OA environment induced by interleukin-1 beta (IL-1 β ), melatonin treatment improved cell proliferation of human chondrocytes, promoted the expression of cartilage ECM proteins (e.g., type II collagen and aggrecan), and inhibited the levels of IL-1 β -induced proteinases, such as matrix metalloproteinase 9 (MMP9), MMP13, ADAMTS4 (a disintegrin and metalloproteinase with thrombospondin motifs 4), and ADAMTS5. Both the microarray and polymerase chain reaction (PCR) experiments revealed that miR-140 was a melatonin-responsive microRNA and melatonin upregulated miR-140 expression, which was suppressed by IL-1 β stimulation. In vivo experiments demonstrated that intra-articular injection of melatonin prevented disruptions of cartilage matrix homeostasis and successfully alleviated the progression of surgery-induced OA in mice. Transfection of miR-140 antagomir completely counteracted the antiarthritic effects of melatonin by promoting matrix destruction. Our findings demonstrate that melatonin protects the articular cartilage from OA-induced degradation by targeting miR-140, and intra-articular administration of melatonin may benefit patients suffering from OA.
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How this classification was reachedexpand
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) | 0.001 | 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) | 0.001 | 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 teacher head, 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".