Statins Against Drug-Induced Nephrotoxicity
Bibliographic record
Abstract
Drug-induced nephrotoxicity (DIN) accounts for up to 60% of hospital acquired acute kidney injury with considerable morbidity and mortality. Several efforts have been made to reduce drug-induced nephrotoxicity; however, DIN remains a matter of concern. Statins with their antioxidant, anti-inflammatory and anti-apoptotic effects may have the potential to protect kidney against DIN. The present review evaluated all of the available in vitro and in vivo studies that examined the use of statins as renoprotective agents against nephrotoxic drugs. Materials for this review were obtained by searching Medline, PubMed, Scopus, Cochrane central register of controlled trials, and Cochrane database of systematic reviews. Key words used as search terms included "statin", "3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitors, "HMG-CoA reductase inhibitors", "nephroprotective", "renoprotective", "drug-induced renal diseases", "drug-induced nephrotoxicity", "drug-induced renal toxicity", "drug-induced nephropathy", "drug-induced renal side effects", and "contrast-induced nephropathy". This search was performed without time limitation. Only English language articles were included in this review. This review concluded that chronic statin user may be less prone to contrast-induced nephropathy (CIN) compared with statin non-users. Short-term high dose statin administration may also reduce the incidence of CIN in statin naïve patients. This renoprotective effect of statins against CIN is seen in low risk patients with normal kidney function or mild kidney dysfunction, but probably not in patients with moderate to severe renal dysfunction. Based on available animal data, statins may protect kidney against gentamicin-, cisplatin- and cyclosporine-induced nephrotoxicity, however, theses animal results have not yet been confirmed by human data. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".