Influence of Bilateral Medial Collateral Ligament Injury on mRNA Expression in Distal Corneal Tissues of Control and Ovariohysterectomized Rabbits
Bibliographic record
Abstract
PURPOSE: Corneal tissues are reported to be impacted by physiological changes (eg, menopause), systemic autoimmune diseases, and osteoarthritic-like conditions. In this study, changes in specific mRNA levels in the cornea after a ligament injury in normal and rabbits subjected to surgical menopause were examined. METHODS: Skeletally mature female rabbits were either sham-operated (control) or were subjected to surgical menopause (OVX). Eight weeks post-OVX, subsets of control and OVX animals were subjected to bilateral injuries to their medial collateral ligaments (MCL) of the knee, and 6 and 14 weeks postinjury, corneal tissues were harvested. Using reverse transcriptase-polymerase chain reaction, mRNA levels for several relevant molecules, including matrix molecules, growth factors, cytokines, proteinases, and hormone receptors, were assessed. RESULTS: mRNA levels for estrogen receptor, decorin, collagens, several growth factors, and inflammatory cytokines decreased in central corneal tissue 6 weeks after distal MCL injury in control animals. The central corneal tissues of animals subjected to OVX alone also exhibited decreases in mRNA levels for a similar set of molecules. When OVX animals were further subjected to MCL injury, the mRNA levels for many of these molecules did not vary from those in the uninjured OVX group. Interestingly, mRNA levels for most molecules were still altered 14 weeks post-MCL injury in the control and OVX animals, a time when the MCL has healed. CONCLUSIONS: Corneal tissues respond to changes resulting from OVX and/or injury. OVX combined with a ligament injury does not appear to have an additive impact on corneal mRNA levels for most of the molecules assessed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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".