The Potential of MicroRNAs as Diagnostic and Prognostic Biomarkers for Mild Traumatic Brain Injury: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: At least 80% of traumatic brain injuries (TBI) are classified as mild. Repetitive mTBI might lead to long-term cognitive and behavioural alterations, or even increasing risks of neurocognitive disorders. Current diagnostic techniques have little benefits on patients with no obvious brain lesions. Altered expression of miRNAs in humans’ biofluids are related to the pathological processes of TBI and can be molecular signature. Objective: Investigate the potential of miRNAs as diagnostic and prognostic biomarkers for mTBI. Material and Methods: This study was reported following the PRISMA criteria. Literature search was carried out in PubMed, ScienceDirect, and ProQuest for relevant articles published prior to January 2021. Both qualitative and quantitative analysis were conducted to determine the diagnostic and prognostic value of miRNAs. Quality of included studies was assessed using Newcastle-Ottawa Scale (NOS). Results: Ten clinical studies reported various miRNAs from serum/plasma, cerebrospinal fluid, and saliva as potential diagnostic and prognostic biomarkers. miRNAs secured from the saliva had the highest diagnostic accuracy (Pooled AUC=0.843; 95% CI [0.802,0.883]; I2=0%; P 0.850) who were more vulnerable to head trauma without apparent lesions on neuroimaging. A common miRNA reported across studies, miR-92a, had very good diagnostic accuracy even when used as a single biomarker (AUC>0.890). Qualitative studies showed that the concentration of miR-425, miR-103a-3p, miR-219a-5p, miR-302d-3p, miR-422a, miR-518f-3p, miR-520d-3p, miR-93, miR-191 and miR-499 were correlated with patients’ clinical outcome. All of included studies showed good quality in terms of selection, comparability and outcome domains. Conclusion: The present study suggested the use of salivary miRNAs as an early diagnostic tool for mTBI. Even though its prognostic value was still undermined, miR-92a was a promising candidate for future diagnostic biomarkers in mTBI.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".