microRNA Biomarkers in Paediatric Infection Diagnostics—Bridging the Gap Between Evidence and Clinical Application: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Distinguishing between bacterial and viral infections in children remains a significant challenge for clinicians. Traditional biomarkers have limited utility, often leading to antibiotic overprescription due to clinician uncertainty. With rising antimicrobial resistance, novel biomarkers are needed to improve diagnosis. This scoping review examines current host miRNA biomarkers for acute bacterial and viral infections in children (0-18), focusing on study methods, diagnostic metrics, and research gaps to support clinical translation. RESULTS: Of the 1147 articles identified, 36 studies were included. Notably, 72.2% of the studies originated from Asia, and the distribution across the paediatric age groups was relatively even. A total of 17 miRNAs were validated in at least two independent studies. Three miRNAs, hsa-miR-182-5p, hsa-miR-363-3p, and hsa-miR-206, were consistently associated with bacterial infection in children. Meanwhile, nine miRNAs were associated with viral infections: hsa-miR-155, hsa-miR-29a-3p, hsa-miR-155-5p, hsa-miR-150-5p, hsa-miR-140-3p, hsa-miR-142-3p, hsa-miR-149-3p, hsa-miR-210-3p, and hsa-miR-34a-5p. Across the 12 studies reporting diagnostic accuracy metrics, miRNA biomarkers exhibited a sensitivity ranging from 70% to 100%, and a specificity ranging from 72% to 100%. The area under the curve across the studies demonstrated a range from 0.62 to 0.99. CONCLUSIONS: This scoping review highlights the potential of miRNA targets for diagnosing paediatric infections when studied rigorously. However, clinical translation is limited by poor adherence to STARD guidelines, lack of robust diagnostic metrics, and study heterogeneity. Many studies were set up with a case-control design, a design that, while highlighting differences, is more likely to identify non-specific biomarkers rather than those that are useful for novel clinical diagnostics.
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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 | 0.015 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".