Evidence map›Paper›PMID 30830413›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society2018

A multidimensional

Jérémy Marchand, Estelle Martineau, Yann Guitton, Bruno Le Bizec, Gaud Dervilly-Pinel, Patrick Giraudeau

Abstract read
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In one paragraph

Article in Metabolomics : Official journal of the Metabolomic Society, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. NMR methods for the analysis of mixtures.Chemical communications (Cambridge, England) · 2022
    Review
  4. Review
  5. Review
  6. Article
  7. Review
  8. Article
  9. Two data pre-processing workflows to facilitate the discovery of biomarkers by 2D NMR metabolomics.Metabolomics : Official journal of the Metabolomic Society · 2019
    Article
  10. Review
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

6 authors.

Jérémy MarchandEBSI Team, Chimie et Interdisciplinarité : Synthèse, Analyse, Modélisation (CEISAM), Université de Nantes, CNRS, UMR 6230, BP 92208, 2 rue de la Houssinière, 44322, Nantes, France.
Estelle MartineauEBSI Team, Chimie et Interdisciplinarité : Synthèse, Analyse, Modélisation (CEISAM), Université de Nantes, CNRS, UMR 6230, BP 92208, 2 rue de la Houssinière, 44322, Nantes, France.
Yann GuittonLaberca, Oniris, INRA, Université Bretagne Loire, 44307, Nantes, France.
Bruno Le BizecLaberca, Oniris, INRA, Université Bretagne Loire, 44307, Nantes, France.
Gaud Dervilly-PinelLaberca, Oniris, INRA, Université Bretagne Loire, 44307, Nantes, France. gaud.dervilly@oniris-nantes.fr.
Patrick GiraudeauEBSI Team, Chimie et Interdisciplinarité : Synthèse, Analyse, Modélisation (CEISAM), Université de Nantes, CNRS, UMR 6230, BP 92208, 2 rue de la Houssinière, 44322, Nantes, France. patrick.giraudeau@univ-nantes.fr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAlthough it is still at a very early stage compared to its mass spectrometry (MS) counterpart, proton nuclear magnetic resonance (NMR) lipidomics is worth being investigated as an original and complementary solution for lipidomics. Dedicated sample preparation protocols and adapted data acquisition methods have to be developed to set up an NMR lipidomics workflow; in particular, the considerable overlap observed for lipid signals on 1D spectra may hamper its applicability.

objectivesThe study describes the development of a complete proton NMR lipidomics workflow for application to serum fingerprinting. It includes the assessment of fast 2D NMR strategies, which, besides reducing signal overlap by spreading the signals along a second dimension, offer compatibility with the high-throughput requirements of food quality characterization.

methodThe robustness of the developed sample preparation protocol is assessed in terms of repeatability and ability to provide informative fingerprints; further, different NMR acquisition schemes-including classical 1D, fast 2D based on non-uniform sampling or ultrafast schemes-are evaluated and compared. Finally, as a proof of concept, the developed workflow is applied to characterize lipid profiles disruption in serum from β-agonists diet fed pigs.

resultsOur results show the ability of the workflow to discriminate efficiently sample groups based on their lipidic profile, while using fast 2D NMR methods in an automated acquisition framework.

conclusionThis work demonstrates the potential of fast multidimensional

Indexed as

AnimalsFemaleFoodFood SafetyLipidsMagnetic Resonance ImagingMagnetic Resonance SpectroscopyMetabolomicsPhenethylaminesSwineWorkflowLipidsPhenethylaminesractopamineFood qualityLipidomicsNMR fingerprintingNon uniform samplingSerumUltrafast NMR

Identifiers

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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.