Leucocyte-derived micro-RNAs as candidate biomarkers in Brugada syndrome
Bibliographic record
Abstract
AIMS: Risk stratification for sudden cardiac death in patients with Brugada syndrome remains a major challenge. Contemporary risk prediction models have only modest predictive value. The aim of this study was to assess the role of micro-RNAs from peripheral blood as candidate biomarkers in Brugada syndrome. METHODS AND RESULTS: In this prospective study, Brugada patients and unaffected control individuals were enrolled for analysis of leucocyte-derived microRNAs (miRNAs) levels. Expression levels of 798 different circulating miRNAs were analysed on the NanoString® nCounter platform. All results were cross-validated by using a quantitative polymerase chain reaction. Micro-RNA expression levels of Brugada patients were compared with clinical data. A total of 21 definite Brugada patients (38% with a history of ventricular arrhythmia or cardiac arrest) and 30 unaffected control individuals were included in the study. Micro-RNA analysis showed a distinct expression profile in Brugada patients with 42 differentially expressed markers (38 up-regulated, 4 down-regulated miRNAs). The symptom status of Brugada patients was associated with a distinct miRNA signature. Micro-RNAs 145-5p and 585-3p were significantly up-regulated in symptomatic Brugada patients (P = 0.04). Incorporating miRNAs 145-5p and 585-3p into a multivariable model demonstrated significantly increased symptom prediction (area under the curve = 0.96; 95% confidence interval: 0.88-1.00). CONCLUSION: Brugada patients display a distinct miRNA expression profile compared with unaffected control individuals. There is also evidence that certain miRNAs (miR-145-5p and miR-585-3p) are associated with the symptom status of Brugada patients. The results suggest the principal utility of leucocyte-derived miRNAs as prognostic biomarkers for Brugada syndrome.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".