Evidence map›Paper›PMID 33795629›Full record

ArticleMedical science monitor : international medical journal of experimental and clinical research2021

In Silico Network Analysis of Ingredients of Cornus officinalis in Osteoporosis.

Feiqi Huang, Huizhi Guo, Yuanbiao Wei, Xiao Zhao, Yangsheng Chen, Zhan Lin, Yanhui Zhou, Ping Sun

Open access · hybridAbstract read
In one paragraph

Article in Medical science monitor : international medical journal of experimental and clinical research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
0.4field-weighted citation impact, top 42% 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

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.

  1. A comprehensive review ofFrontiers in nutrition · 2023
    Pooled it
  2. Inhibitory Effect ofMedicina (Kaunas, Lithuania) · 2022
    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

8 authors at 4 institutions in 1 country.

Feiqi HuangFirst Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China (mainland).
Huizhi GuoFirst Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China (mainland).
Yuanbiao WeiDepartment of Bone Orthopedics, The First Affiliated Hospital/School of Clinical Medicine of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China (mainland).
Xiao ZhaoDepartment of Bone Orthopedics, The First Affiliated Hospital/School of Clinical Medicine of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China (mainland).
Yangsheng ChenDepartment of Bone Orthopedics, The First Affiliated Hospital/School of Clinical Medicine of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China (mainland).
Zhan LinDepartment of Bone Orthopedics, The First Affiliated Hospital/School of Clinical Medicine of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China (mainland).
Yanhui ZhouDepartment of Bone Orthopedics, The First Affiliated Hospital/School of Clinical Medicine of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China (mainland).
Ping SunDepartment of Endocrinology, The First Affiliated Hospital/School of Clinical Medicine of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China (mainland).
Guangdong Pharmaceutical University · CNFirst Affiliated Hospital of Guangdong Pharmaceutical University · CNFirst Affiliated Hospital of Guangzhou University of Chinese Medicine · CNGuangzhou University of Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND Cornus officinalis (CO), also known as 'Shanzhuyu', is one of the most common traditional Chinese herbs used against osteoporosis. Although previous studies have found that CO has beneficial effects in alleviating osteoporosis, its mechanisms remain unclear. MATERIAL AND METHODS In this study, we applied system bioinformatic approaches to investigate the possible therapeutic mechanisms of CO against osteoporosis. We collected the active ingredients of CO and their targets from the TCMSP, BATMAN-TCM, and ETCM databases. Next, we obtained the osteoporosis targets from differentially expressed mRNAs from the Gene Expression Omnibus (GEO) gene series (GSE35958). Next, the shared genes of the CO pharmacological targets and osteoporosis-related targets were selected to construct the protein-protein interaction network, based on the results from the STRING database. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out by using the clusterProfiler package in R software. RESULTS In all, there were 58 unique CO compounds and 518 therapeutic targets. Based on the GO and KEGG enrichment results of 98 common genes, we selected the top 25 terms, based on the terms' P values. We found that the anti-osteoporotic effect of CO may mostly involve the regulation of calcium metabolism and reactive oxygen species, and the estrogen signaling pathway and osteoclast differentiation pathway. CONCLUSIONS We found the possible mechanisms of CO in treating osteoporosis may be based on multiple targets and pathways. We also provided a theoretical basis and promising direction for investigating the exact anti-osteoporotic mechanisms of CO.

Indexed as

Medicine, Chinese TraditionalCell DifferentiationComputational BiologyComputer SimulationCornusDrugs, Chinese HerbalEstrogensGene OntologyHumansMolecular Docking SimulationOsteoclastsOsteoporosisProtein Interaction MapsReactive Oxygen SpeciesSignal TransductionDrugs, Chinese HerbalEstrogensReactive Oxygen Species

Identifiers

PMID33795629
PMCPMC8023278
OpenAlexW3129483532

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.