Evidence map›Paper›PMID 35571895›Full record

ArticleFrontiers in nutrition2022

Studies on Bioactive Components of Red Ginseng by UHPLC-MS and Its Effect on Lipid Metabolism of Type 2 Diabetes Mellitus.

Rensong Huang, Meng Zhang, Yu Tong, Yaran Teng, Hui Li, Wei Wu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in nutrition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-weighted citation impact, top 29% 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

5 citing papers in PubMed, 11 citations in OpenAlex.

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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

6 authors at 1 institution in 1 country.

Rensong HuangChangchun University of Chinese Medicine, Jilin Ginseng Academy, Changchun, China.
Meng ZhangChangchun University of Chinese Medicine, Jilin Ginseng Academy, Changchun, China.
Yu TongChangchun University of Chinese Medicine, Jilin Ginseng Academy, Changchun, China.
Yaran TengChangchun University of Chinese Medicine, Jilin Ginseng Academy, Changchun, China.
Hui LiChangchun University of Chinese Medicine, Jilin Ginseng Academy, Changchun, China.
Wei WuChangchun University of Chinese Medicine, Jilin Ginseng Academy, Changchun, China.
Changchun University of Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Red ginseng is a processed product of Methods: An ultrahigh performance liquid chromatography coupled with quadrupole time of flight mass spectrometry (UHPLC-Q-TOF-MS) technology was applied to quantitatively analyze RGW. A T2DM rat model was established to verify the activity of RGW in treating lipid metabolism disorders caused by diabetes. First, the changes in diabetes-related parameters were observed, then the biochemical parameters of the rat serum and liver were measured, and finally, the pathological sections of the rat liver were observed, and the content of short-chain fatty acids in stools was measured. The Results: A total of 10 ginsenosides were identified and quantitatively analyzed in RGW. Experimental results demonstrated that RGW can improve lipid metabolism disorders. RGW significantly reduced the fasting blood glucose and TG and TC levels in T2DM rats, and hepatic steatosis was significantly ameliorated. Conclusion: The total RGW were quantitatively analyzed by UHPLC-MS, and its effect on lipid metabolism of T2DM was studied. The experiment demonstrated that red ginseng can regulate lipid metabolism and improve lipid deposition, which provides a promising development for red ginseng as a functional food.

Indexed as

lipid metabolismred ginsengshort-chain fatty acidstype 2 diabetes mellitusUHPLC-MS

Identifiers

PMID35571895
PMCPMC9094573
OpenAlexW4285742311

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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.