Systematic review of microRNAs in human acute kidney injury
Bibliographic record
Abstract
Introduction Early diagnosis of acute kidney injury (AKI) is limited with current tools. MicroRNAs (miRNAs) are implicated in AKI pathogenesis in preclinical models, but less is known about their role in humans. We conducted a systematic review to identify dysregulated miRNAs in humans with AKI.Methods We searched Ovid MEDLINE, Embase, Web of Science, and CENTRAL (August 21, 2023) for studies of human subjects with AKI. We excluded reviews and pre-clinical studies without human data. The primary outcome was dysregulated miRNAs in AKI. Two reviewers screened abstracts, reviewed full texts, performed data extraction and quality assessment (Newcastle Ottawa Scale).Results We screened 2,456 reports and included 92 for synthesis without meta-analysis. All studies except one were observational. Studies were grouped by etiology of AKI: cardiac surgery-associated (CS-AKI, n = 13 studies), sepsis (n = 25), nephrotoxic (n = 9), kidney transplant (n = 26), and other causes (n = 19). In total, 128 miRNAs were identified to be dysregulated across AKI studies (45 miRNAs upregulated, 55 downregulated, 28 both). miR-21 was the most frequently reported (n = 17 studies) and it was increased in all etiologies except CS-AKI where it was decreased (n = 3 studies). Study limitations included bias due to targeted approaches, absence of clinical data/controls, and miRNA normalization methods. Overall study quality was fair (median 5/9, range 2-8 points).Conclusion Dysregulated miRNAs, particularly miR-21, have potential as AKI biomarkers. These results should be interpreted cautiously due to methodological limitations. Standardized methods and unbiased approaches are needed to validate candidate miRNA biomarkers.Registration: International Prospective Register of Systematic Reviews (PROSPERO CRD42020201253)
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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 | 0.022 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.010 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".