Evidence map›Paper›PMID 29654535›Full record

ArticleJournal of the American Society for Mass Spectrometry2018

Detecting Protein-Glycolipid Interactions Using CaR-ESI-MS and Model Membranes: Comparison of Pre-loaded and Passively Loaded Picodiscs.

Jun Li, Ling Han, Jianing Li, Elena N Kitova, Zi Jian Xiong, Gilbert G Privé, John S Klassen

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of the American Society for Mass Spectrometry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.0field-weighted citation impact, top 24% 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

7 citing papers in PubMed, 18 citations in OpenAlex.

  1. The Nanodisc System for Investigating Protein-Lipid Interactions.Methods in molecular biology (Clifton, N.J.) · 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

7 authors at 3 institutions in 1 country.

Jun LiAlberta Glycomics Centre and Department of Chemistry, University of Alberta, Edmonton, Alberta, T6G 2G2, Canada.
Ling HanAlberta Glycomics Centre and Department of Chemistry, University of Alberta, Edmonton, Alberta, T6G 2G2, Canada.
Jianing LiAlberta Glycomics Centre and Department of Chemistry, University of Alberta, Edmonton, Alberta, T6G 2G2, Canada.
Elena N KitovaAlberta Glycomics Centre and Department of Chemistry, University of Alberta, Edmonton, Alberta, T6G 2G2, Canada.
Zi Jian XiongDepartment of Biochemistry, University of Toronto, Toronto, Ontario, M5S 1A8, Canada.
Gilbert G PrivéDepartment of Biochemistry, University of Toronto, Toronto, Ontario, M5S 1A8, Canada.
John S KlassenAlberta Glycomics Centre and Department of Chemistry, University of Alberta, Edmonton, Alberta, T6G 2G2, Canada. john.klassen@ualberta.ca.
University of Alberta · CAUniversity of Toronto · CAAlberta Glycomics Centre · CA

Funding

Alberta Innovates - Technology Futures Alberta Glycomics CentreAlberta Innovates - Technology Futures Graduate student scholarship
6 · The paper itself

Abstract

Catch-and-release electrospray ionization mass spectrometry (CaR-ESI-MS), implemented using model membranes (MMs), is a promising approach for the discovery of glycolipid ligands of glycan-binding proteins (GBPs). Picodiscs (PDs), which are lipid-transporting complexes composed of the human sphingolipid activator protein saposin A and phospholipids, have proven to be useful MMs for such studies. The present work compares the use of conventional (pre-loaded) PDs with passively loaded PDs (

Indexed as

Catch-and-release electrospray ionization mass spectrometryGlycan-binding proteinGlycolipidModel membranesNanodiscsPicodiscsScreening

Identifiers

PMID29654535
OpenAlexW2797154150

What OpenQuestion holds

Textmetadata
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.