Evidence map›Paper›PMID 29403901›Full record

ArticleJournal of pharmaceutical analysis2014

UPLC-Q-TOF/MS based metabolomic profiling of serum and urine of hyperlipidemic rats induced by high fat diet.

Qiong Wu, Hai Zhang, Xin Dong, Xiao-Fei Chen, Zhen-Yu Zhu, Zhan-Ying Hong, Yi-Feng Chai

Open access · goldAbstract read
In one paragraph

Article in Journal of pharmaceutical analysis, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
0.9field-weighted citation impact, top 27% 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

22 citing papers in PubMed, 46 citations in OpenAlex.

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  16. Metabolomic profiling of liver tissues after acute administration of vardenafil in rats.Veterinary research forum : an international quarterly journal · 2020
    Article
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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

7 authors at 1 institution in 1 country.

Qiong WuSchool of Pharmacy, Second Military Medical University, Shanghai 200433, China.
Hai ZhangSchool of Pharmacy, Second Military Medical University, Shanghai 200433, China.
Xin DongSchool of Pharmacy, Second Military Medical University, Shanghai 200433, China.
Xiao-Fei ChenSchool of Pharmacy, Second Military Medical University, Shanghai 200433, China.
Zhen-Yu ZhuSchool of Pharmacy, Second Military Medical University, Shanghai 200433, China.
Zhan-Ying HongSchool of Pharmacy, Second Military Medical University, Shanghai 200433, China.
Yi-Feng ChaiSchool of Pharmacy, Second Military Medical University, Shanghai 200433, China.
Second Military Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hyperlipidemia is considered to be a high lipid level in blood, can induce metabolic disorders and dysfunctions of the body, and results in some severe complications. Therefore, hunting for some metabolite markers and clarifying the metabolic pathways in vivo will be an important strategy in the treatment and prevention of hyperlipidemia. In this study, a rat model of hyperlipidemia was constructed according to histopathological data and biochemical parameters, and the metabolites of serum and urine were analyzed by UPLC-Q-TOF/MS. Combining pattern recognition and statistical analysis, 19 candidate biomarkers were screened and identified. These changed metabolites indicated that during the development and progression of hyperlipidemia, energy metabolism, lipid metabolism, amino acid metabolism and nucleotide metabolism were mainly disturbed, which are reported to be closely related to diabetes, cardiovascular diseases, etc. This study demonstrated that a UPLC-Q-TOF/MS based metabolomic approach is useful to profile the alternation of endogenous metabolites of hyperlipidemia.

Indexed as

HyperlipidemiaMetabolomicPattern recognitionUPLC-Q-TOF/MS

Identifiers

PMID29403901
PMCPMC5761356
OpenAlexW2091614884

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.