Evidence map›Paper›PMID 33175875›Full record

ArticlePloS one2020

Urine metabolomics of rats with chronic atrophic gastritis.

Guo-Xiu Zu, Qian-Qian Sun, Jian Chen, Xi-Jian Liu, Ke-Yun Sun, Liang-Kun Zhang, Ling Li, Tao Han, Hai-Liang Huang

Abstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Current pharmaceutical design · 2026
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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

9 authors.

Guo-Xiu ZuDepartment of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.ORCID 0000-0002-7773-0568
Qian-Qian SunDepartment of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
Jian ChenDepartment of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
Xi-Jian LiuDepartment of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
Ke-Yun SunDepartment of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
Liang-Kun ZhangDepartment of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
Ling LiDepartment of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
Tao HanGraduate Office, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
Hai-Liang HuangDepartment of Rehabilitation Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

aimTo use liquid chromatography-mass spectrometry (LC-MS) to identify endogenous differential metabolites in the urine of rats with chronic atrophic gastritis (CAG). MATERIALS AND

methodsMethylnitronitrosoguanidine (MNNG) was used to produce a CAG model in Wistar rats, and HE staining was used to determine the pathological model. LC-MS was used to detect the differential metabolic profiles in rat urine. Diversified analysis was performed by the statistical method.

resultsCompared with the control group, the model group had 68 differential metabolites, 25 that were upregulated and 43 that were downregulated. The main metabolic pathways were D-glutamine and D-glutamic acid metabolism, histidine metabolism and purine metabolism.

conclusionBy searching for differential metabolites and metabolic pathways in the urine of CAG rats, this study provides effective experimental data for the pathogenesis and clinical diagnosis of CAG.

Indexed as

Disease Models, AnimalMetabolic Networks and PathwaysAnimalsBiomarkersGastritis, AtrophicMaleMetabolomicsRatsRats, WistarBiomarkers

Identifiers

PMID33175875
PMCPMC7657567

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