Comprehensive Expression Analysis of microRNAs and mRNAs in Synovial Tissue from a Mouse Model of Early Post-Traumatic Osteoarthritis
Bibliographic record
Abstract
To better understand the molecular processes involved in driving osteoarthritis disease progression we characterized expression profiles of microRNAs (miRNA) and mRNAs in synovial tissue from a post-traumatic OA mouse model. OA was induced in 10-12 week old male C57BL6 mice by bilateral surgical destabilization of the medial meniscus (DMM). RNA isolated from the anterior synovium of mice at 1 and 6 weeks post-surgery was subject to expression profiling using Agilent microarrays and qPCR. OA severity was determined histologically. Anterior and posterior synovitis decreased with post-operative time after sham and DMM. No differences in synovitis parameters were evident between sham and DMM in the anterior synovium at either time. While expression profiling revealed 394 miRNAs were dysregulated between 1 and 6 week time-points in the anterior synovium, there were no significant changes in miRNA or mRNA expression between DMM and sham mice at both time-points. Bioinformatic analysis of the miRNAs and mRNAs differentially expressed in tandem with the resolution of anterior synovial inflammation revealed similar biological processes and functions, including organismal injury, connective tissue disorder and inflammatory responses. Our data demonstrates that early OA-specific patterns of synovial miRNAs or mRNAs dysregulation could not be identified in this model of post-traumatic OA.
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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 | 0.001 | 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".