Plasma miR-26a as a Diagnostic Biomarker Regulates Cytokine Expression in Systemic Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: We sought to identify specific microRNA (miRNA) for systemic juvenile idiopathic arthritis (sJIA) and to determine the involvement of these miRNA in regulating the expression of cytokines. METHODS: Microarray profiling was performed to identify differentially expressed miRNA in sJIA plasma. Levels of candidate miRNA and mRNA were assessed by real-time PCR, and cytokines were measured by ELISA. Dual-luciferase reporter assay was used to validate the direct interaction between miR-26a and interleukin 6 (IL-6). RESULTS: Forty-eight miRNA were differentially expressed in the plasma of patients with sJIA compared with healthy controls (HC). Five miRNA were selected for further validation. The expression level of miR-26a was exclusively elevated in the plasma of patients with sJIA as compared with 4 rheumatic diseases and 2 subtypes of JIA (oligoarticular and polyarticular). The levels of IL-6, IL-1β, and tumor necrosis factor-α in the plasma of patients with sJIA were increased, and only IL-6 presented a positive correlation with miR-26a (r = 0.539, p < 0.0001). After stimulation with IL-6, miR-26a expression was upregulated in THP-1 cells, while the supernatant level of IL-6 was downregulated by transfection of miR-26a mimics. Consistently, direct target relationship between miR-26a and IL-6 was confirmed. CONCLUSION: This study demonstrates that miR-26a is expressed specifically and highly in sJIA plasma and suggests that miR-26a may regulate the levels of cytokines in sJIA. Our findings highlight miR-26a as a potential biomarker for the diagnosis as well as differential diagnosis of sJIA.
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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 | 0.001 |
| 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 | 0.000 |
| 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".