Evidence map›Paper›PMID 35677380›Full record

ArticleEvidence-based complementary and alternative medicine : eCAM2022

Effects of Herbal Therapy on Intestinal Microbiota and Serum Metabolomics in Different Rat Models of Mongolian Medicine.

Guniang Jiu, Riao Dao, Dongxing Wu, Wang Hung, Haburi Jin, Li Li, Xiquan Fu, Chula Sa, Eerdunchaolu

Abstract read
In one paragraph

Article in Evidence-based complementary and alternative medicine : eCAM, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. From gut to proteomics: the impact ofFrontiers in oncology · 2025
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4 · The record

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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

9 authors.

Guniang JiuMongolian Medical College, Inner Mongolia Minzu University, Tongliao 028000, Inner Mongolia, China.
Riao DaoDepartment of Psychosomatic Medicine, International Mongolian Medical Hospital of Inner Mongolia Autonomous Region, Hohhot 010000, Inner Mongolia, China.
Dongxing WuMongolian Medical College, Inner Mongolia Minzu University, Tongliao 028000, Inner Mongolia, China.
Wang HungMongolian Medical College, Inner Mongolia Minzu University, Tongliao 028000, Inner Mongolia, China.
Haburi JinAffiliated Hospital of Inner Mongolia Minzu University, Rehabilitation Hospital, Tongliao 028000, Inner Mongolia, China.
Li LiSecond Department of Encephalopathy, Affiliated Hospital of Inner Mongolia Minzu University, Tongliao 028000, Inner Mongolia, China.
Xiquan FuMongolian Medical College, Inner Mongolia Minzu University, Tongliao 028000, Inner Mongolia, China.
Chula SaMongolian Medical College, Inner Mongolia Minzu University, Tongliao 028000, Inner Mongolia, China.
EerdunchaoluMongolian Medical College, Inner Mongolia Minzu University, Tongliao 028000, Inner Mongolia, China.ORCID https://orcid.org/0000-0003-1147-9769

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Heyi disease, Xila disease, and Badagan disease are three common diseases in Mongolian medicine. The changes in intestinal microbiota may be associated with the occurrence, development, and treatment of these diseases. This study aimed to investigate the effects of herbal treatment on intestinal microbiota and serum metabolites in rats with these three diseases. Methods: Firstly, Heyi, Xila, and Badagan disease model rats were established by environmental, diet, and drug intervention. Then, 16S rRNA gene sequencing and metabolomics analysis were used to analyze the changes in intestinal microbiota and serum metabolites after treatment. PICRUSt analysis was applied to predict the potential functions of intestinal microbiota, and OPLS-DA multivariate model was applied to screen differential serum metabolites. Results: 16S rRNA gene sequencing showed that herbal treatment significantly increased the species diversity and changed the composition of intestinal microbiota in Heyi disease and Xila disease rats. After treatment, there were 10, 9, and 3 bacterial biomarkers that were increased in Heyi, Xila, and Badagan disease rats, respectively. In the Heyi disease model, treatment resulted in 45 differential serum metabolites, involving 4 pathways. In the Badagan disease model, treatment resulted in 62 differential serum metabolites, involving 4 pathways. However, there was no significant difference in serum metabolites between TreatB and ConB in the Xila disease model. Conclusions: Herbal treatment significantly changed the intestinal microbiota and serum metabolites of rats with three Mongolian medicine diseases.

Identifiers

PMID35677380
PMCPMC9170395

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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.