The Circulating Cell-free microRNA Profile in Systemic Sclerosis Is Distinct from Both Healthy Controls and Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To evaluate the expression profile of cell-free circulating microRNA (miRNA) in systemic sclerosis (SSc), healthy controls (HC), and systemic lupus erythematosus (SLE). METHODS: Total RNA was purified from plasma and 45 different, mature miRNA were measured using quantitative PCR assays after reverse transcription. Samples (n = 189) were from patients with SSc (n = 120), SLE (n = 29), and from HC (n = 40). Expression data were clustered by principal components analysis, and diagnostically specific miRNA profiles were developed by leave-one-out cross-validation. Diagnostic probability scores were derived from stepwise logistic regression. RESULTS: Thirty-seven miRNA specificities were consistently detected and 26 of these were unaffected by SSc sample age and present in more than two-thirds of SSc samples. SSc cases showed a distinct expression profile with 14/26 miRNA significantly decreased (false discovery rate < 0.05) and 5/26 increased compared with HC. A 21-miRNA classifier gave optimum accuracy (80%) for discriminating SSc from both HC and SLE. The discrimination between HC and SSc (95% accuracy) was strongly driven by miRNA of the 17 ∼ 92 cluster and by miR-16, -223, and -638, while SLE and SSc differed mainly in the expression of miR-142-3p, -150, -223, and -638. Except for a weak correlation between anti-Scl-70 and miR-638 (p = 0.048), there were no correlations with other patient variables. CONCLUSION: Circulating miRNA profiles are characteristic for SSc compared with both HC and SLE cases. Some of the predicted targets of the differentially regulated miRNA are of relevance for transforming growth factor-β signaling and fibrosis, but need to be validated in independent studies.
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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".