Follow-up of COLL2-1, COLL2-1NO2 and myeloperoxydase in dogs after transection of the cruciate ligament of the knee
Bibliographic record
Abstract
Purpose: To determine the profile of Coll2-1, Coll2-1NO2 and myeloperoxydase (MPO) serum concentrations in experimental knee OA induced in the dog by transection of the anterior cruciate ligament. Methods: Surgical transection of the ACL of the right knee was performed on 16 adult crossbred dogs. The dogs were sacrificed 8 weeks after the surgical procedure. Coll2-1, Coll2-1NO2 and MPO were measured by specific immunoassays in 16 dogs at baseline and every 2 weeks during the 8 weeks. The results were expressed as median (range). Results: Immunostainings with D3 and D37, the antiserum recognizing Coll2-1 and Coll2-1NO2, respectively, labelled extracellular matrix in the superficial layer of fibrillated cartilage. After the transection of the ACL, the concentration of 3 biomarkers increased significantly (Friedman test: p<0.001). The concentrations of Coll2-1 and MPO were significantly increased at week 2 compared to baseline [Coll2-1 baseline: 281.57 (131.02-384.67) nM vs Coll2-1 week 2: 345.52 (181.15-589.25) nM (p<0.01) and MPO baseline: 5.16 (<0.4-14.7) ng/ml vs MPO week 2: 14.54 (3.28-31.50) ng/ml (p<0.001)] and remained stable until week 8 [Coll2-1 week 8:318.89 (117.95-492.28) nM and MPO week 8: 11.55 (2.87-42.94) ng/ml]. The Coll2-1NO2 concentration increased significantly at weeks 6 and 8 compared to baseline [Coll2-1NO2 baseline: 0.54 (0.29-1.48) nM vs Coll2-1NO2 week 6: 0.64 (0.40-1.9) nM (p<0.001) vs week 8: 0.61 (0.37-1.79) nM]. Conclusions: These findings suggest that Coll2-1 is a relevant marker for the detection of early structural changes in OA dogs. Interestingly, MPO and Coll2-1NO2 are increased in OA dogs indicating that an oxidative stress occurs in this OA model.
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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 | 0.000 | 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 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".