Absolute concentrations of mRNA for type I and type VI collagen in the canine meniscus in normal and ACL‐deficient knee joints obtained by RNase protection assay
Bibliographic record
Abstract
Relatively little is known about the cellular and molecular responses of the knee joint meniscus to joint injury, despite the functional importance of the tissue. We investigated how meniscus cells respond to joint injury in the early stages of post-traumatic osteoarthritis by characterizing the changes in matrix gene expression in menisci at 3 and 12 weeks post-surgery in dogs in which the anterior cruciate ligament (ACL) in one joint was transected and the other unoperated joint served as a control. Changes in the total RNA and DNA concentrations of the menisci were determined. Absolute concentrations of the mRNA of the COL1A1 gene of type 1 collagen, the major fibrillar collagen of the meniscus, and the COL6A3 gene of type VI collagen, a major repair molecule, were determined by quantitative ribonuclease (RNase) protection assay. The concentration of total RNA in medial and lateral menisci increased from 40 to 60 microg RNA/g wet wt in unoperated, control joints to 200-350 microg RNA/g wet wt in ACL-deficient joints. No significant changes were detected in the concentration of DNA (900-1200 microg DNA/g wet wt). Low concentrations of COL1A1 (2-3 pmol mRNA/g DNA) and COL6A3 (0.3-0.6 pmol mRNA/g DNA) mRNA transcripts were measured in normal menisci. ACL-deficiency induced a 20-38 fold increase in COL1A1 and COL6A3 mRNA concentration at 3 weeks, and an 11-19 fold increase at 12 weeks post-surgery. In general, the increase in COL1A1 and COL6A3 mRNA concentrations was greater in medial menisci than in lateral menisci. These results demonstrate that the menisci initiate a vigorous biosynthetic response to transection of the ACL.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".