Evidence map›Paper›PMID 27695416›Full record

ArticleFrontiers in pharmacology2016

Kidney Tissue Targeted Metabolic Profiling of Unilateral Ureteral Obstruction Rats by NMR.

Zhenyu Li, Aiping Li, Jining Gao, Hong Li, Xuemei Qin

Open access · goldAbstract read
In one paragraph

Article in Frontiers in pharmacology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
3.3field-weighted citation impact, top 8% of its field
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

13 citing papers in PubMed, 29 citations in OpenAlex.

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  13. Frontiers in pharmacology · 2017
    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

5 authors at 2 institutions in 1 country.

Zhenyu LiModern Research Center for Traditional Chinese Medicine, Shanxi University Taiyuan, China.
Aiping LiModern Research Center for Traditional Chinese Medicine, Shanxi University Taiyuan, China.
Jining GaoShanxi Hospital of Integrated Traditional and Western Medicine Taiyuan, China.
Hong LiShanxi Hospital of Integrated Traditional and Western Medicine Taiyuan, China.
Xuemei QinModern Research Center for Traditional Chinese Medicine, Shanxi University Taiyuan, China.
Shanxi University of Traditional Chinese Medicine · CNShanxi Province Hospital of Traditional Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Renal interstitial fibrosis is a common pathological process in the progression of kidney disease. A nuclear magnetic resonance (NMR) based metabolomic approach was used to analyze the kidney tissues of rats with renal interstitial fibrosis (RIF), induced by unilateral ureteral obstruction (UUO). The combination of a variety of statistical methods were used to screen out 14 significantly changed potential metabolites, which are related with multiple biochemical processes including amino acid metabolism, adenine metabolism, energy metabolism, osmolyte change and induced oxidative stress. The exploration of the contralateral kidneys enhanced the understanding of the disease, which was also supported by serum biochemistry and kidney histopathology results. In addition, the pathological parameters (clinical chemistry, histological and immunohistochemistry results) were correlated with the significantly changed differential metabolites related with RIF. This study showed that targeted tissue metabolomic analysis can be used as a useful tool to understand the mechanism of the disease and provide a novel insight in the pathogenesis of RIF.

Indexed as

1H NMRkidneymetabolomicsrenal interstitial fibrosisunilateral ureteral obstruction

Identifiers

PMID27695416
PMCPMC5023943
OpenAlexW2519076352

What OpenQuestion holds

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LicenceCC BY
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.