Evidence map›Paper›PMID 34723029›Full record

ArticleACS omega2021

Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry for the Analysis of Complex Compounds in Serum and Its Application in Accurate Detection of Early Arsenic Exposure.

Nan Jing, Xinzheng Wang, Xin Yang, Qian Liu, Hongxia Wang, Fangting Dong, Kun He, Na Wang

Open access · goldAbstract read
In one paragraph

Article in ACS omega, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 38% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. 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

8 authors at 1 institution in 1 country.

Nan JingNational Center for Biomedical Analysis, 27 Tai-Ping Road, Beijing 100850, China.ORCID https://orcid.org/0000-0001-9837-5705
Xinzheng WangNational Center for Biomedical Analysis, 27 Tai-Ping Road, Beijing 100850, China.
Xin YangNational Center for Biomedical Analysis, 27 Tai-Ping Road, Beijing 100850, China.
Qian LiuNational Center for Biomedical Analysis, 27 Tai-Ping Road, Beijing 100850, China.
Hongxia WangNational Center for Biomedical Analysis, 27 Tai-Ping Road, Beijing 100850, China.ORCID https://orcid.org/0000-0002-9820-9261
Fangting DongNational Center for Biomedical Analysis, 27 Tai-Ping Road, Beijing 100850, China.
Kun HeNational Center for Biomedical Analysis, 27 Tai-Ping Road, Beijing 100850, China.
Na WangNational Center for Biomedical Analysis, 27 Tai-Ping Road, Beijing 100850, China.
National Center of Biomedical Analysis · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the acceleration of industrialization, environmental arsenic pollution is threatening human health. However, by the time clinical symptoms appear, arsenic toxicity has usually caused irreversible damage to the body, so it is important to establish a rapid and accurate screening method for early arsenic exposure. In this work, 32 female C57BL/6 mice were exposed to different concentrations of inorganic arsenic in drinking water for a week. By analyzing the changes in serum, more than 20 compounds were detected to increase or decrease with the increase of arsenic intake. The abnormal increase in inosine, xanthine, xanthosine, and hypoxanthine and the abnormal purine pathway were found at the same time. Dimethylarsenic acid, an important inorganic arsenic metabolite in the body, was also found in serum. Combined with statistical analysis, early arsenic exposure can be easily and quickly detected, and the potential health risks of short-term exposure can be revealed simultaneously.

Identifiers

PMID34723029
PMCPMC8552457
OpenAlexW3207888242

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.