In vivo effect of opticin deficiency in cartilage in a surgically induced mouse model of osteoarthritis
Bibliographic record
Abstract
Abstract The SLRP opticin (OPTC) has been demonstrated to be produced and degraded in osteoarthritic (OA) human cartilage. Here, we investigated the in vivo effect of OPTC deficiency in OA cartilage. OA was induced in 10-week-old Optc−/− and Optc+/+ mice. Ten weeks post-surgery, cartilage was processed for histology and immunohistochemistry. SLRP expression was determined in non-operated mouse cartilage. OA Optc−/− demonstrated significant protection against cartilage degradation. Data revealed that in non-operated Optc−/− cartilage, expression of SLRPs lumican and epiphycan was up-regulated at day 3 and in 10-week-olds (p ≤ 0.039), and fibromodulin down-regulated in 10-week-olds (p = 0.001). Immunohistochemistry of OA mice showed a similar pattern. In OA Optc−/− cartilage, markers of degradation and complement factors were all down-regulated (p ≤ 0.038). In OA Optc−/− cartilage, collagen fibers were thinner and better organized (p = 0.038) than in OA Optc+/+ cartilage. The protective effect of OPTC deficiency during OA results from an overexpression of lumican and epiphycan, known to bind and protect collagen fibers, and a decrease in fibromodulin, contributing to a reduction in the complement activation/inflammatory process. This work suggests that the evaluation of the composition of the different SLRPs in OA cartilage could be applied as a new tool for OA prognosis classification.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".