MicroRNAs obtained from cervical swabs in predicting preterm birth
Bibliographic record
Abstract
HIGHLIGHTS Increased expression of certain miRNAs in women experiencing preterm birth could be linked to various molecular pathways which contributes to preterm birth. miRNAs obtained from cervical swabs exhibit statistically significant difference in expression between women with term births and preterm births. ABSTRACT Objective: Identifying the risk of preterm birth is crucial for early intervention. miRNAs, small noncoding RNAs that regulate gene expression, play a key role in development and tissue maintenance. Under stress conditions, their regulatory functions become significant, linking them to disease states. Using miRNAs from cervical swabs as potential biomarkers could revolutionize preterm birth risk assessment. This systematic review examines current research on the effectiveness of cervical swab miRNAs in predicting and estimating preterm birth risks, aiming to enhance early detection and management strategies for preterm births. Materials and Methods: Using the PubMed database, 14 articles were obtained using the keywords “microRNA” and “preterm”. Reviews and unrelated studies were then excluded from both pooled articles, resulting in 4 articles included in the final review. Risk of bias were examined using the Newcastle Ottawa Scale. Sample characteristics, time of cervical swab collection, and results from each study were extracted for further analysis. Results: A total of 4 articles were included in this review. Various miRNAs were examined in and were generally successful in predicting preterm birth. miRNA-145, miRNA-199, miRNA-30, miRNA-21, and miRNA-181 family were examined by multiple studies and produced significant results in predicting preterm birth. Based on enrichment analysis, various miRNAs were found to be involved in several biomolecular signaling pathways leading to preterm birth, such as inflammation, chemokine and cytokine signaling pathway, and toll-like receptor signaling. Conclusion: miRNAs obtained from cervical swabs exhibit statistically significant difference in expression between women with term births and preterm births. Further studies are needed to improve the predicting power and accuracy of miRNAs in preterm births.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".