Licofelone reduces progression of structural changes in a canine model of osteoarthritis under curative conditions: effect on protease expression and activity.
Bibliographic record
Abstract
OBJECTIVE: We investigated the effectiveness of licofelone, a combined 5-lipoxygenase and cyclooxygenase inhibitor, on structural changes in the anterior cruciate ligament (ACL) experimental dog model of osteoarthritis (OA) under therapeutic conditions. The effect of drug treatment on the expression and activity of metalloproteases in the OA cartilage was also studied. METHODS: The cranial cruciate ligament of the right stifle joint was surgically sectioned in 14 dogs to create OA lesions. Of these dogs, 7 received placebo treatment and served as OA controls, while 7 were treated with licofelone 2.5 mg/kg twice daily for an 8-week period, starting 4 weeks after surgery. At necropsy, macroscopic evaluations were made of the size of osteophytes and the severity of cartilage lesions on femoral condyles and tibial plateaus. Collagenase and other metalloprotease activity levels in cartilage were measured. Levels of gene expression of matrix metalloprotease (MMP-1), MMP-13, cathepsin K, and ADAMTS-5 were quantified by RT-PCR. RESULTS: Licofelone treatment reduced the development of osteophytes and size of cartilage lesions on the femoral condyles and on the tibial plateaus (p < 0.04). Drug treatment also significantly decreased collagenase (p < 0.02) and metalloprotease (p < 0.04) activities, as well as the levels of gene expression of MMP-1 (p < 0.01), MMP-13 (p < 0.05), cathepsin K, and ADAMTS-5 (p = 0.01). CONCLUSION: Under therapeutic conditions licofelone showed the ability to reduce the progression of structural changes in experimental dog OA. This beneficial effect is likely mediated through decrease in the synthesis of a number of catabolic factors, including proteolytic enzymes, involved in cartilage breakdown.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".