Effect of oral glucosamine on cartilage and meniscus in normal and chymopapain‐injected knees of young rabbits
Bibliographic record
Abstract
OBJECTIVE: To determine if oral glucosamine (GlcN) improves joint biology after acute damage by a protease. METHODS: The effect of 8 weeks of dietary GlcN (20 or 100 mg/kg/day) on knee joint cartilage was evaluated in 2.2-kg male NZW rabbits with and without damage introduced by intraarticular injection of chymopapain (CP). Cartilage was evaluated histologically and scored according to the Mankin scale. Analyses of total hydroxyproline and glycosaminoglycan (GAG) contents and reverse transcription-polymerase chain reaction (RT-PCR) analysis of selected genes were performed. RESULTS: After 8 weeks, there was no effect of GlcN on the GAG content of normal cartilage. Both levels of GlcN treatment significantly increased the sulfated GAG content in the cartilage of the medial femoral condyle in damaged and contralateral knees, but did not change the collagen content. In CP-injected knees, there was still some loss of surface proteoglycan (PG) that was not completely corrected by dietary GlcN. Even after 8 weeks, levels of messenger RNA (mRNA) detected by RT-PCR showed changes indicative of damage and repair, such as elevated type II collagen mRNA, and these levels were not influenced by GlcN treatment. Meniscal GAG content was increased in the contralateral knee of rabbits receiving high-dose GlcN, but was decreased in those receiving no GlcN or low-dose GlcN. Neither diet nor treatment affected the meniscal collagen content. CONCLUSION: These results suggest that oral GlcN treatment might be useful in a situation where GlcN is limiting, such as where there is a rapid replacement of cartilage PG.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".