Roles and Mechanisms of Irisin in Attenuating Pathological Features of Osteoarthritis
Bibliographic record
Abstract
To investigate the effects and mechanisms of irisin, a newly discovered myokine, in cartilage development, osteoarthritis (OA) pathophysiology and its therapeutic potential for treating OA we applied the following five strategical analyses using (1) murine joint tissues at different developmental stages; (2) human normal and OA pathological tissue samples; (3) experimental OA mouse model; (4) irisin gene knockout (KO) and knock in (KI) mouse lines and their cartilage cells; (5) in vitro mechanistic experiments. We found that Irisin was involved in all stages of cartilage development. Both human and mouse OA tissues showed a decreased expression of irisin. Intra-articular injection of irisin attenuated ACLT-induced OA progression. Irisin knockout mice developed severe OA while irisin overexpression in both irisin KI mice and intraarticular injection of irisin protein attenuated OA progression. Irisin inhibited inflammation and promoted anabolism in chondrogenic ADTC5 cells. Proliferative potential of primary chondrocytes from KI mice was found to be enhanced, while KO mice showed an inhibition under normal or inflammatory conditions. The primary chondrocytes from irisin KI mice showed reduced expression of inflammatory factors and the chondrocytes isolated from KO mice showed an opposite pattern. In conclusion, it is the first time to show that irisin is involved in cartilage development and OA pathogenesis. Irisin has the potential to ameliorate OA progression by decreasing cartilage degradation and inhibiting inflammation, which could lead to the development of a novel therapeutic target for treating bone and cartilage disorders including osteoarthritis.
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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 | 0.001 |
| 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".