Molecular Response of Rabbit Menisci to Surgically Induced Hemarthrosis and a Single Intra‐Articular Dexamethasone Treatment
Bibliographic record
Abstract
Anterior cruciate ligament reconstructive surgery can restore biomechanical stability, however, such surgery cannot reliably prevent the onset of post-traumatic osteoarthritis. The aim of this study was to elucidate the molecular response that occurs within the menisci following a surgical injury that allows bleeding into the joint space, and then to investigate the effect of dexamethasone (DEX) on this molecular response. Cell viability studies following acute controlled exposure to blood and blood plus DEX were also conducted. Forty-eight New Zealand white rabbits were randomly allocated into control, sham, surgical, and surgical + DEX groups (each group n = 6). Animals were sacrificed at 48 h and 9 weeks, and menisci were harvested. The messenger RNA (mRNA) expression levels for key inflammatory, and degradative proteins, as well as mRNA levels for autophagy pathway molecules were quantified, and statistically significant changes were described. Meniscal cell viability was calculated by incubating groups of medial and lateral menisci in autologous blood, or autologous blood plus DEX for 48 h (each group n = 4; total of eight medial and eight lateral menisci), and then conducting a histological live/dead assay. Results indicated a significant reduction in only medial meniscal cell viability when the tissue was exposed to blood in combination with DEX. A single administration of DEX following surgery significantly suppresses the elevated molecular expression for key inflammatory and degradative markers within menisci at 48 h and 9 weeks post-surgery. In vitro, autologous blood did not affect cell viability, but addition of DEX uniquely impacted the medial menisci. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:2043-2052, 2019.
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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 | 0.001 |
| 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".