Evidence map›Paper›PMID 36818638›Full record

ArticleEuropean journal of lipid science and technology : EJLST2023

Detection and Semi-quantification of Lipids on High-Performance Thin-Layer Chromatography Plate using Ceric Ammonium Molybdate Staining.

Kesatebrhan Haile Asressu, Qibin Zhang

Abstract read
In one paragraph

Article in European journal of lipid science and technology : EJLST, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Kesatebrhan Haile AsressuCenter for Translational Biomedical Research, University of North Carolina at Greensboro, North Carolina Research Campus, Kannapolis, NC 28081, USA.
Qibin ZhangCenter for Translational Biomedical Research, University of North Carolina at Greensboro, North Carolina Research Campus, Kannapolis, NC 28081, USA.

Funding

Critical Lipid Species in the Hepatovirus LifecycleR01AI150095 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LEMON, STANLEY M. · 2020 to 2024
$2.6M
Lipidome Remodeling During Development of T1DR01DK123499 · NIDDK · UNIVERSITY OF NORTH CAROLINA GREENSBORO · PI ZHANG, QIBIN · 2020 to 2024
$1.4M
NIAID NIH HHS R01 AI150095NIDDK NIH HHS R01 DK123499
6 · The paper itself

Abstract

It is desirable to quickly check the composition of lipids in small size samples, but achieving this is challenging using the existing staining methods. Herein, we developed a highly sensitive and semi-quantitative method for analysis of lipid samples with ceric ammonium molybdate (CAM) staining. The CAM detection method was systematically evaluated with a wide range of lipid classes including phospholipids, sphingolipids, glycerolipids, fatty acids (FA) and sterols, demonstrating high sensitivity, stability, and overall efficiency. Additionally, CAM staining provides a clean yellow background in high performance thin-layer chromatography (HPTLC) which facilitates quantification of lipids using image processing software. Lipids can be stained with CAM reagent regardless of their head group types, position of the carbon-carbon double bonds, geometric isomerism and the variation in the length of FA chain, but staining is mostly affected by the degree of unsaturation of the FA backbone. The mechanism of the CAM staining of lipids was proposed on principles of the reduction-oxidation reaction, in which Mo(VI) oxidizes the unsaturated lipids into carbonyl compounds on the HPTLC plate upon heating, while itself being reduced to Mo(IV). This method was applied for the separation, identification, and quantification of lipid extracts from porcine brain.

Indexed as

CAM stainHPTLClipidsMo reduction-oxidationporcine brain

Identifiers

PMID36818638
PMCPMC9937734

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Registered trials

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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.