Exploring the role of non-coding RNAs in atrial septal defect pathogenesis: A systematic review
Bibliographic record
Abstract
BACKGROUND: Extensive research has recognized the significant roles of non-coding RNAs (ncRNAs) in various cellular pathophysiological processes and their association with diverse diseases, including atrial septal defect (ASD), one of the most prevalent congenital heart diseases. This systematic review aims to explore the intricate involvement and significance of ncRNAs in the pathogenesis and progression of ASD. METHODS: Four databases (PubMed, Embase, Scopus, and the Web of Science) were searched systematically up to June 19, 2023, with no year restriction. The risk of bias assessment was evaluated using the Newcastle-Ottawa scale. RESULTS: The present systematic review included thirteen studies with a collective study population of 874 individuals diagnosed with ASD, 21 parents of ASD patients, and 22 pregnant women carrying ASD fetuses. Our analysis revealed evidence linking five long ncRNAs (STX18-AS1, HOTAIR, AA709223, BX478947, and Moshe) and several microRNAs (hsa-miR-19a, hsa-miR-19b, hsa-miR-375, hsa-miR-29c, miR-29, miR-143/145, miR-17-92, miR-106b-25, and miR-503/424, miR-9, miR-30a, miR-196a2, miR-139-5p, hsa-let-7a, hsa-let-7b, and hsa-miR-486) to ASD progression, corresponding to previous studies. CONCLUSIONS: NcRNAs play a crucial role in unraveling the underlying mechanisms of ASD, contributing to both biomarker discovery and therapeutic advancements. This systematic review sheds light on the mechanisms of action of key ncRNAs involved in ASD progression, providing valuable insights for future research in this field.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".