Evidence map›Paper›PMID 38884043›Full record

ArticleCurrent opinion in toxicology2022

Natural products for the prevention of antibiotic-associated kidney injury.

Marshall Yuan, Kelsey Briscese, Thomas S Hong, Luigi Brunetti

Abstract read
In one paragraph

Article in Current opinion in toxicology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Pharmaceuticals (Basel, Switzerland) · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Marshall YuanDepartment of Pharmacy Practice and Administration, Ernest Mario School of Pharmacy, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
Kelsey BrisceseDepartment of Pharmacy Practice and Administration, Ernest Mario School of Pharmacy, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
Thomas S HongRobert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
Luigi BrunettiDepartment of Pharmacy Practice and Administration, Ernest Mario School of Pharmacy, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.

Funding

Racial differences in MT1/MT2 receptor expression in human kidneysR01DK131214 · NIDDK · RUTGERS, THE STATE UNIV OF N.J. · PI Luigi Brunetti · 2022 to 2026
$2.2M
NIDDK NIH HHS R01 DK131214
6 · The paper itself

Abstract

Drug-induced acute kidney injury (AKI), especially from exposure to antibiotics, has a high prevalence secondary to their frequent prescription. Typically, drug-induced AKI results from acute tubular necrosis or acute interstitial nephritis. While some risk factors for the development of AKI in individuals treated with antibiotics are modifiable, others such as concomitant drug therapies to treat comorbidities, age, and pre-existing chronic kidney disease are not modifiable. As such, there is an urgent need to identify strategies to reduce the risk of AKI in individuals requiring antibiotic therapy. Natural products, especially those rich in active constituents possessing antioxidant properties are an attractive option to mitigate AKI risk. Given that mitochondrial dysfunction precedes AKI and natural products can restore mitochondrial health and counter the oxidative stress secondary to mitochondrial damage investigating their utility warrants further attention. The following review summarizes the available preclinical and clinical evidence that provides a foundation for future study.

Indexed as

Acute kidney injuryAntibioticsNatural productsOxidative stress

Identifiers

PMID38884043
PMCPMC11178348

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.