The role of MicroRNAs in arrhythmogenic right ventricular cardiomyopathy: A systematic review
Bibliographic record
Abstract
BACKGROUND: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a rare inherited heart condition with structural and functional abnormalities of the right ventricle. Microribonucleic acids (miRNAs, miRs) could be a solution in detecting ARVC earlier, more commonly, and in a less invasive way. AIMS: We aimed to systematically review the current knowledge about the role of miRNAs in ARVC. METHODS: Primary original research written in English assessing miRNAs in ARVC were included. Systematic reviews, meta-analyses, reviews, case reports, letters to editors, commentaries, conference abstracts, guidelines/statements, expert opinions, pre-prints, and book chapters were excluded at the screening stage. Five databases were searched: Embase, Medline Ultimate, PubMed, Scopus, and Web of Science, last on October 4, 2024. Eventually, 3 13 original studies relevant to the discussed area were included. The quality of research was assessed with the Newcastle-Ottawa Scale. RESULTS: MiR-216a was consistently increased in mice ARVC models and in patients suffering from this disease. Based on the reviewed literature, miR-1, miR-21, and miR-122 are other most important miRNAs in the ARVC. Nevertheless, the research that has already been performed on these miRNAs gives evidence only for their diagnostic potential. Bioinformatic analyses revealed that the following miRNAs are the most important ones involved in ARVC: let-7b, miR-10b-5p, miR-15a-5p, miR-21-5p, miR-29b-3p, miR-122-5p, miR-144-3p, miR-149-5p, miR-182-5p, miR-186-5p, miR-320a, miR-494-3p, and miR-590-3p. CONCLUSIONS: Creating a miRNA panel that could identify ARVC patients with high sensitivity and specificity would be helpful. Currently, there are many gaps in the existing knowledge, which makes miRNA in ARVC an attractive field for future investigation.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.007 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".