ArticleBMC complementary medicine and therapies2021
Obese rats intervened with Rhizoma coptidis revealed differential gene expression and microbiota by serum metabolomics.
Article in BMC complementary medicine and therapies, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Who cites it
6 citing papers in PubMed, 11 citations in OpenAlex.
- The effect of fructose exposure on amino acid metabolism among Chinese community residents and its possible multi-omics mechanisms.Scientific reports · 2023Article
- Qingyi granules ameliorate severe acute pancreatitis in rats by modulating the gut microbiota and serum metabolic aberrations.Pharmaceutical biology · 2023Article
- Berberine Reduces Lipid Accumulation in Obesity via Mediating Transcriptional Function of PPARδ.International journal of molecular sciences · 2023Article
- Efficacy of Kushen decoction on high-fat-diet-induced hyperlipidemia in rats.Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan · 2022Article
- A novel biocompatible Eu-based coordination polymers of cytarabine anticancer drug: Preparation, luminescence properties andFrontiers in chemistry · 2022Article
- Gut fungi differentially response to the antipyretic (heat-clearing) and diaphoretic (exterior-releasing) traditional Chinese medicines inFrontiers in pharmacology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundIntegrating systems biology is an approach for investigating metabolic diseases in humans. However, few studies use this approach to investigate the mechanism by which Rhizoma coptidis (RC) reduces the effect of lipids and glucose on high-fat induced obesity in rats.
methodsTwenty-four specific pathogen-free (SPF) male Sprague-Dawley rats (80 ± 10 g) were used in this study. Serum metabolomics were detected by ultra-high-performance liquid chromatography coupled with quadrupole-time-of-flight tandem mass spectrometry. Liver tissue and cecum feces were used for RNA-Seq technology and 16S rRNA gene sequencing, respectively.
resultsWe identified nine potential biomarkers, which are differential metabolites in the Control, Model and RC groups, including linoleic acid, eicosapentaenoic acid, arachidonic acid, stearic acid, and L-Alloisoleucine (p < 0.01). The liver tissue gene expression profile indicated the circadian rhythm pathway was significantly affected by RC (Q ≤ 0.05). A total of 149 and 39 operational taxonomic units (OTUs), which were highly associated with biochemical indicators and potential biomarkers in the cecum samples (FDR ≤ 0.05), respectively, were identified.
conclusionThis work provides information to better understand the mechanism of the effect of RC intervention on hyperlipidemia and hypoglycemic effects in obese rats. The present study demonstrates that integrating systems biology may be a powerful tool to reveal the complexity of metabolic diseases in rats intervened by traditional Chinese medicine.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.