Evidence map›Paper›PMID 39327656›Full record

ReviewSe pu = Chinese journal of chromatography2024

[Advances in the application of ion chromatography-mass spectrometry in the fields of life and health].

Yu-Xin Yi, Mi-Cong Jin

Abstract readReviewEnglish Abstract
In one paragraph

Review in Se pu = Chinese journal of chromatography, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Yu-Xin YiNingbo Municipal Center for Disease Control and Prevention, Key Laboratory of Health Risk Appraisal for Trace Toxic Chemicals of Zhejiang Province, Ningbo 315010, China.
Mi-Cong JinNingbo Municipal Center for Disease Control and Prevention, Key Laboratory of Health Risk Appraisal for Trace Toxic Chemicals of Zhejiang Province, Ningbo 315010, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ion chromatography is a technique commonly used to separate strongly polar and ionizable substances; it can be used to separate, identify, and quantify ionizable compounds in complex samples when coupled with mass spectrometry, and is currently being used in the application of food analysis, drug analysis, metabolomics and clinical poisoning analysis. Herein, we review the development of ion chromatography-mass spectrometry (IC-MS), its progress over the past 20 years, and future trends in the abovementioned areas. The IC-MS research progress and applications for the determination of inorganic anions, organic acids, polar pesticides, biogenic amines, and sugars in the food field are discussed. Drug analysis applications are discussed mainly in relation to the analysis of drug impurities, identifying drug degradation products, and determination of plasma concentration, while the separation and analysis of strongly polar metabolites, such as organic acids, sugar phosphates, and nucleotides in biological matrices are discussed in relation to metabolomics. Advances in the analysis of strongly polar or ionizable toxic compounds, such as alkyl methylphosphonic acid, methylphosphonic acid, glyphosate, 3-nitropropionic acid, and indandione rodenticides, are mainly discussed in clinical poisoning analysis field. This paper is expected to become a useful reference for the further expansion and application of IC-MS in the life and health fields.

Indexed as

Mass SpectrometryChromatography, Ion ExchangeFood AnalysisHumansMetabolomicsclinical poisoning analysisdrug analysisfood analysision chromatography-mass spectrometry (IC-MS)metabolomicsreview

Identifiers

PMID39327656
PMCPMC11439138

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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.