Early joint tissue changes are highly correlated with a set of inflammatory and degradative synovial biomarkers after ACL autograft and its sham surgery in an ovine model
Bibliographic record
Abstract
While impossible in humans, the mechanisms of early cartilage, bone and meniscal damage can be quantified after anterior cruciate ligament (ACL) injury in animal models. We utilized an ovine model to determine if the mRNA expression of inflammatory and degradative molecules (IL-1β, IL-6, MMP-1, 2, 3, and 13) in the synovium correlated to changes in joint tissues 2 weeks post-ACL surgery, to test the hypothesis that synovial inflammation is a marker of these changes and possibly their originator. Nine "idealized" ACL autografts were performed and compared with three sham and six normal animals. Using validated protocols, early osteophyte formation, articular cartilage, and meniscal damage were quantified. Synovium was harvested and mRNA expression quantified using qPCR. Multiple linear regression analysis (MLRA) was utilized to correlate synovial mRNA expression in treated and contra-lateral limbs, from all treatment groups with corresponding joint scores. Synovial mRNA expression was significantly elevated in all experimental and sham joints. The MLRA model was a significant predictive tool (p = 0.001, R(2) = 0.70) of gross tissue scores with significant contributions from IL-1β, IL-6, and MMP-3. Findings suggest that this set of synovial biomarkers is predictive (p < 0.009) of early gross changes of joint tissues after arthrotomy and likely directly involved in the relevant mechanisms, particularly early osteophyte formation, in vivo.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".