Serum and Urinary Cell–free MiR-146a and MiR-155 in Patients with Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Recent studies showed that micro-RNA play important roles in the pathogenesis of autoimmune diseases. We studied the levels of miR-146a and miR-155 in the serum and urinary supernatant of patients with systemic lupus erythematosus (SLE). METHODS: The serum and urinary supernatant levels of miR-146a and miR-155 were determined by real-time quantitative polymerase chain reaction in 40 patients with SLE and 30 healthy controls. RESULTS: Compared to controls, serum miR-146a and miR-155 levels were lower, and the urinary level of miR-146a was higher, in SLE. Estimated glomerular filtration rate (eGFR) correlated with both serum miR-146a (r = 0.519, p = 0.001) and miR-155 (r = 0.384, p = 0.014). Serum miR-146a inversely correlated with proteinuria (r = -0.341, p = 0.031) and the SLE Disease Activity Index (r = -0.465, p = 0.003). Serum miR-146a and miR-155 levels also correlated with red blood cell count, platelet count, and lymphocyte count. After treatment with calcitriol for 6 months, serum miR-146a level of SLE patients increased significantly (p < 0.001), and its change inversely correlated with the level of calcium-phosphate product (r = -0.466, p = 0.003). CONCLUSION: The results suggested that serum miR-146a and miR-155 participate in the pathophysiology of SLE and might be used as biomarkers of SLE.
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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 | 0.001 | 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 | 0.000 |
| 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".