Differentially Expressed Inflammation-Regulating MicroRNAs in Oligoarticular Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: To evaluate microRNA expression in synovial fluid (SF), plasma, and leukocytes from patients with juvenile idiopathic arthritis (JIA). METHODS: MicroRNA expression in pooled JIA plasma and SF was assessed by absolute quantitative droplet digital PCR array. The results were validated in individual patient samples. MicroRNA content in leukocytes and extracellular vesicles was evaluated by real-time PCR in JIA blood and SF. Blood microRNA expression was compared with healthy controls (HCs). Principal component analysis was used to profile JIA plasma and SF microRNAs, and the potential biological consequences of microRNA dysregulation were investigated by pathway analysis. RESULTS: MiR-15a-5p and miR-409-3p levels were higher in JIA plasma than in HC plasma. JIA SF contained elevated levels of miR-21-5p, miR-27a-3p, miR-146b-5p, miR-155-5p, and miR-423-5p, and decreased miR-192-5p and miR-451a, compared to JIA plasma. Extracellular vesicle analysis demonstrated variable encapsulation among selected microRNAs, with only miR-155-5p being represented substantially in extracellular vesicles. SF leukocytes also had higher expression of miR-21-5p, miR-27a-3p, miR-146b-5p, and miR-155-5p, and lower expression of miR-409-3p and miR-451a, relative to blood. No differences were observed between JIA and HC blood leukocytes. Clusters of microRNAs were commonly altered in JIA joint fluid and leukocytes compared to JIA blood samples. In silico analysis predicted that differentially expressed microRNAs in JIA target the transforming growth factor (TGF)-β pathway. CONCLUSION: The expression of multiple microRNAs is dysregulated in JIA both locally and systemically, which may inhibit the TGF-β pathway. These findings advance our knowledge of JIA immunopathogenesis and may lead to the development of targeted therapies.
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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 | 0.000 | 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) | 0.000 | 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".