Therapeutic effects of micro-RNAs in preclinical studies of acute kidney injury: a systematic review and meta-analysis
Bibliographic record
Abstract
AKI has a high mortality rate, may lead to chronic kidney disease, and effective therapies are lacking. Micro-RNAs (miRNAs) regulate biologic processes by potently inhibiting protein expression, and pre-clinical studies have explored their roles in AKI. We conducted a systematic review and meta-analysis of miRNAs as therapeutics in pre-clinical AKI. Study screening, data extraction, and quality assessments were performed by 2 independent reviewers. Seventy studies involving 42 miRNA species were included in the analysis. All studies demonstrated significant effects of the miRNA intervention on kidney function and/or histology, with most implicating apoptosis and phosphatase and tensin homolog (PTEN) signaling. Fourteen studies (20.0%) examined the effect of miRNA-21 in AKI, and meta-analysis demonstrated significant increases in serum creatinine and kidney injury scores with miR-21 antagonism and pre-conditioning. No studies reported on adverse effects of miRNA therapy. Limitations also included lack of model diversity (100% rodents, 61.4% ischemia-reperfusion injury), and predominance of male sex (78.6%). Most studies had an unclear risk of bias, and the majority of miRNA-21 studies were conducted by a single team of investigators. In summary, several miRNAs target kidney function and apoptosis in pre-clinical AKI models, with data suggesting that miRNA-21 may mediate protection and kidney repair.Systematic review registration ID: CRD42019128854.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.022 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".