Impact of Age, Systemic Glucocorticoids, and Progressive Knee Arthritis on Specific mRNA Levels in Different Areas of the Rabbit Cornea
Bibliographic record
Abstract
PURPOSE: To determine the influence of factors such as age, osteoarthritis (OA), and glucocorticoid treatment on total RNA and mRNA regulation in the cornea and how these factors differ between prepupillary and peripheral areas of the cornea. METHODS: Molecular analyses of corneal tissue were performed using rabbits of different age groups and skeletally mature animals that had undergone anterior cruciate ligament (ACL) transection, an established model of knee OA. Systemic glucocorticoids were administered to cohorts of the osteoarthritic and control animals to determine the influence of distal joint disease on the corneal response. Corneal tissue was analyzed for changes in mRNA levels for several relevant genes: collagen I, collagen III, collagen V, decorin core protein, cyclooxygenase-2 (COX-2), glucocorticoid receptor, and the housekeeping gene beta-actin. RESULTS: The corneal tissue was found to have diminishing total RNA with age, which is consistent with previous studies in the literature. Interestingly, in skeletally mature animals, distal joint OA was found to affect corneal mRNA levels for several important structural and inflammatory genes (collagen I, decorin core protein, and COX-2) in a manner that progressed with OA progression. Although systemic glucocorticoid treatment did not alter mRNA levels in the normal cornea, it did counteract the changes observed early after OA induction (3 weeks) while having less of an effect in later, more established arthritis (6 weeks). CONCLUSIONS: This study reveals that distal joint OA can affect mRNA levels for several structural and inflammatory genes of the cornea, changes that seem to be suppressed by systemic glucocorticoid treatment. These findings indicate that OA has associated systemic factors that influence corneal cell metabolism.
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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 | 0.001 | 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".