Evidence map›Paper›PMID 32906195›Full record

ReviewElectrophoresis2021

CE-MS for metabolomics: Developments and applications in the period 2018-2020.

Wei Zhang, Rawi Ramautar

Open access · hybridAbstract readReview
In one paragraph

Review in Electrophoresis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed, 1 pooled it
5.8field-weighted citation impact, top 3% 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

45 citing papers in PubMed, 1 synthesis or guideline pooled it, 109 citations in OpenAlex.

  1. Pooled it
  2. Review
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  4. Artificial intelligence to investigate metabolomics data for precision medicine.Metabolomics : Official journal of the Metabolomic Society · 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

2 authors at 1 institution in 1 country.

Wei ZhangBiomedical Microscale Analytics, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.
Rawi RamautarBiomedical Microscale Analytics, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.ORCID 0000-0002-1673-4848
Leiden University · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Capillary electrophoresis-mass spectrometry (CE-MS) is now a mature analytical technique in metabolomics, notably for the efficient profiling of polar and charged metabolites. Over the past few years, (further) progress has been made in the design of improved interfacing techniques for coupling CE to MS; also, in the development of CE-MS approaches for profiling metabolites in volume-restricted samples, and in strategies that further enhance the metabolic coverage. In this article, which is a follow-up of a previous review article covering the years 2016-2018 (Electrophoresis 2019, 40, 165-179), the main (technological) developments in CE-MS methods and strategies for metabolomics are discussed covering the literature from July 2018 to June 2020. Representative examples highlight the utility of CE-MS in the fields of biomedical, clinical, microbial, plant and food metabolomics. A complete overview of recent CE-MS-based metabolomics studies is given in a table, which provides information on sample type and pretreatment, capillary coatings, and MS detection mode. Finally, some general conclusions and perspectives are given.

Indexed as

Electrophoresis, CapillaryMass SpectrometryMetabolomicsAnimalsBiomarkersHumansMicePlantsYeastsBiomarkersBiomedical and clinicalMass spectrometryMetabolomicsmicrobial and plantTechnological developments

Identifiers

PMID32906195
PMCPMC7891659
OpenAlexW3086777877

What OpenQuestion holds

Textmetadata
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.