Evidence map›Paper›PMID 34948437›Full record

ReviewInternational journal of molecular sciences2021

Advances in Lipid Extraction Methods-A Review.

Ramesh Kumar Saini, Parchuri Prasad, Xiaomin Shang, Young-Soo Keum

Abstract readReview
In one paragraph

Review in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 122 papers.

0numbers the graph read from it
0cells of the map it votes in
122citing 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

122 citing papers in PubMed.

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  20. Review

62 more citing papers are in PubMed but not listed here.

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

4 authors.

Ramesh Kumar SainiDepartment of Crop Science, Konkuk University, Seoul 143-701, Korea.ORCID 0000-0001-9052-9941
Parchuri PrasadInstitute of Biological Chemistry, Washington State University, Pullman, WA 99164, USA.ORCID 0000-0002-9648-7487
Xiaomin ShangJilin Provincial Key Laboratory of Nutrition and Functional Food, Jilin University, Changchun 130062, China.
Young-Soo KeumDepartment of Crop Science, Konkuk University, Seoul 143-701, Korea.

Funding

National Research Foundation of Korea 2019R1G1A1006815
6 · The paper itself

Abstract

Extraction of lipids from biological tissues is a crucial step in lipid analysis. The selection of appropriate solvent is the most critical factor in the efficient extraction of lipids. A mixture of polar (to disrupt the protein-lipid complexes) and nonpolar (to dissolve the neutral lipids) solvents are precisely selected to extract lipids efficiently. In addition, the disintegration of complex and rigid cell-wall of plants, fungi, and microalgal cells by various mechanical, chemical, and enzymatic treatments facilitate the solvent penetration and extraction of lipids. This review discusses the chloroform/methanol-based classical lipid extraction methods and modern modifications of these methods in terms of using healthy and environmentally safe solvents and rapid single-step extraction. At the same time, some adaptations were made to recover the specific lipids. In addition, the high throughput lipid extraction methodologies used for liquid chromatography-mass spectrometry (LC-MS)-based plant and animal lipidomics were discussed. The advantages and disadvantages of various pretreatments and extraction methods were also illustrated. Moreover, the emerging green solvents-based lipid extraction method, including supercritical CO

Indexed as

AnimalsCell WallChloroformChromatography, LiquidGreen Chemistry TechnologyLipidomicsLipidsMass SpectrometryMethanolSolventsChloroformLipidsMethanolSolventsBligh and Dyer methodFolch methodgreen solventslipidomicspre-treatmentsSoxhlet extractionsupercritical CO2 extraction

Identifiers

PMID34948437
PMCPMC8704327

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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.