Evidence map›Paper›PMID 34822429›Full record

ArticleMetabolites2021

Coupling Mixed Mode Chromatography/ESI Negative MS Detection with Message-Passing Neural Network Modeling for Enhanced Metabolome Coverage and Structural Identification.

Gang Xing, Vishnu Sresht, Zhongyuan Sun, Yuji Shi, Michelle F Clasquin

Open access · goldAbstract read
In one paragraph

Article in Metabolites, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 43% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Review
  3. 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

5 authors at 1 institution in 1 country.

Gang XingInternal Medicine Research Unit, Pfizer Worldwide Research, Development & Medical, Cambridge, MA 02139, USA.ORCID 0000-0001-8857-9421
Vishnu SreshtSimulation and Modeling Sciences, Pfizer Worldwide Research, Development & Medical, Cambridge, MA 02139, USA.
Zhongyuan SunInternal Medicine Research Unit, Pfizer Worldwide Research, Development & Medical, Cambridge, MA 02139, USA.
Yuji ShiInternal Medicine Research Unit, Pfizer Worldwide Research, Development & Medical, Cambridge, MA 02139, USA.
Michelle F ClasquinInternal Medicine Research Unit, Pfizer Worldwide Research, Development & Medical, Cambridge, MA 02139, USA.ORCID 0000-0002-5361-6859
Pfizer (United States) · US

Funding

Pfizer (United States) Worldwide Research, Development & Medical (WRDM)
6 · The paper itself

Abstract

A key unmet need in metabolomics continues to be the specific, selective, accurate detection of traditionally difficult to retain molecules including simple sugars, sugar phosphates, carboxylic acids, and related amino acids. Designed to retain the metabolites of central carbon metabolism, this Mixed Mode (MM) chromatography applies varied pH, salt concentration and organic content to a positively charged quaternary amine polyvinyl alcohol stationary phase. This MM method is capable of separating glucose from fructose, and four hexose monophosphates a single chromatographic run. Coupled to a QExactive Orbitrap Mass Spectrometer with negative ESI, linearity, LLOD, %CV, and mass accuracy were assessed using 33 metabolite standards. The standards were linear on average >3 orders of magnitude (R

Indexed as

LCMSmachine learningmetabolomicsMPNNretention time prediction

Identifiers

PMID34822429
PMCPMC8620857
OpenAlexW3211633973

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.