Non-coding RNAs as diagnostic biomarkers for preeclampsia: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Preeclampsia (PE), a grave obstetric complication, mandates the expeditious formulation of efficacious early diagnostic strategies. Accumulating evidence suggests that non - coding RNAs (ncRNAs), which are present in maternal circulation and placental tissues, display abnormal expression patterns in patients with PE, underscoring their potential as diagnostic biomarkers. This systematic review and meta - analysis intends to assess the diagnostic accuracy of ncRNAs for the detection of PE. Methods A comprehensive search was carried out across seven databases (China National Knowledge Infrastructure [CNKI], Wanfang Database, VIP Database, PubMed, Web of Science, Embase, and the Cochrane Library) up to December 25, 2024, to identify case - control and cohort studies exploring the diagnostic value of ncRNAs in PE. The quality of the studies was evaluated using the Quality Assessment of Diagnostic Accuracy Studies − 2 (QUADAS − 2) tool and the Newcastle - Ottawa Scale (NOS), and publication bias was assessed using Deeks’ funnel plot. The pooled sensitivity (SEN), specificity (SPE), diagnostic odds ratio (DOR), and area under the curve (AUC) were computed using Review Manager 5.4 and Meta - DiSc 1.4. Results Among the 2,201 identified studies, 40 fulfilled the inclusion criteria for qualitative synthesis. Forty - eight ncRNAs showed diagnostic potential, including 25 microRNAs (miRNAs), 9 long non - coding RNAs (lncRNAs), and 6 circular RNAs (circRNAs). The pooled sensitivity and specificity were 80% (95% confidence interval [CI]: 76–84%) and 82% (95% CI: 79–85%), respectively. Single miRNA assays presented superior diagnostic performance (sensitivity [SEN]: 85%, specificity [SPE]: 85%) in comparison to circRNAs (SEN: 80%, SPE: 79%). Notably, combinatorial panels consisting of 2–3 ncRNAs attained optimal diagnostic performance, with a sensitivity of 91% (95% CI: 88–94%), a specificity of 80% (95% CI: 76–84%), and an area under the curve (AUC) of 0.9418 (standard error [SE] = 0.0152). Conclusion Circulating ncRNAs exhibit significant potential as diagnostic biomarkers for PE, with multi-analyte panels providing improved diagnostic accuracy compared to single-marker strategies.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".