Influence of a single systemic corticosteroid injection on mRNA levels for a subset of genes in connective tissues of the rabbit knee: a comparison of steroid types and effect of skeletal maturity.
Bibliographic record
Abstract
OBJECTIVE: . To determine the effect of glucocorticoid treatment on mRNA levels for matrix molecules and enzymes in knee connective tissues from skeletally mature and skeletally immature rabbits. METHODS: Intraarticular and extraarticular connective tissues of the knee were collected from skeletally mature or immature rabbits at 72 and/or 24 h postinjection of a single intramuscular inoculation of 10 mg/kg methylprednisolone or dexamethasone (skeletally mature rabbits) or 1 mg/kg (skeletally immature rabbits). Total RNA was isolated and mRNA levels for matrix molecules, matrix metalloproteinases (MMP) and their inhibitors, cyclooxygenase-2, transforming growth factor-ss, glucocorticoid receptor, and heat shock protein 90 alpha and beta were assessed by RT-PCR. RESULTS: Glucocorticoid treatment resulted in significant alterations in mRNA levels for a specific subset of genes in a tissue-specific and time-dependent manner in both maturity groups. Most notably, glucocorticoid treatment resulted in significant suppression of mRNA levels for collagens I and III, and MMP-3 and MMP-13. CONCLUSION: mRNA levels for both anabolic genes (collagens) and catabolic genes (MMP) in connective tissues are rapidly affected by systemic glucocorticoid treatment irrespective of skeletal maturity. This glucocorticoid sensitivity of normal tissues may lead to unwanted bystander effects when corticosteroids are used therapeutically, effects that could contribute to a negative influence on the functioning of such tissues.
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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 | 0.000 | 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".