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Identification of key genes and pathways using bioinformatics analysis in septic shock children

Overview of attention for article published in Infection and Drug Resistance, August 2018
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  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

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18 Mendeley
Title
Identification of key genes and pathways using bioinformatics analysis in septic shock children
Published in
Infection and Drug Resistance, August 2018
DOI 10.2147/idr.s157269
Pubmed ID
Authors

Junting Yang, Shunwen Zhang, Jie Zhang, Jiangtao Dong, Jiangdong Wu, Le Zhang, Peng Guo, Suyu Tang, Zhengyong Zhao, Hongzhou Wang, Yanheng Zhao, Wanjiang Zhang, Fang Wu

Abstract

Sepsis is still one of the reasons for serious infectious diseases in pediatric intensive care unit patients despite the use of anti-infective therapy and organ support therapy. As it is well-known, the effect of single gene or pathway does not play a role in sepsis. We want to explore the interaction of two more genes or pathways in sepsis patients for future works. We hypothesize that the discovery from the available gene expression data of pediatric sepsis patients could know the process or improve the situation. The gene expression profile dataset GSE26440 of 98 septic shock samples and 32 normal samples using whole blood-derived RNA samples were generated. A total of 1,108 upregulated and 142 downregulated differentially expressed genes (DEGs) were identified in septic shock children using R software packages. The Gene Ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were analyzed using DAVID software; Gene Set Enrichment Analysis method was also used for enrichment analysis of the DEGs. The protein-protein interaction (PPI) network and the top 10 hub genes construction of the DEGs were constructed via plug-in Molecular Complex Detection and cytoHubba of Cytoscape software. From the PPI network, the top 10 hub genes, which are all upregulated DEGs in the septic shock children, were identified as GAPDH, TNF, EGF, MAPK3, IL-10, TLR4, MAPK14, IL-1β, PIK3CB, and TLR2. Some of them were involved in one or more significant inflammatory pathways, such as the enrichment of tumor necrosis factor (TNF) pathway in the activation of mitogen-activated protein kinase activity, toll-like receptor signaling pathway, nuclear factor-κB signaling pathway, PI3K-Akt signaling pathway, and TNF signaling pathway. These findings support future studies on pediatric septic shock.

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X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 28%
Researcher 2 11%
Student > Bachelor 2 11%
Other 1 6%
Student > Master 1 6%
Other 1 6%
Unknown 6 33%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 28%
Medicine and Dentistry 2 11%
Agricultural and Biological Sciences 1 6%
Veterinary Science and Veterinary Medicine 1 6%
Sports and Recreations 1 6%
Other 1 6%
Unknown 7 39%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 August 2018.
All research outputs
#13,387,978
of 23,099,576 outputs
Outputs from Infection and Drug Resistance
#397
of 1,698 outputs
Outputs of similar age
#164,478
of 331,040 outputs
Outputs of similar age from Infection and Drug Resistance
#21
of 57 outputs
Altmetric has tracked 23,099,576 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,698 research outputs from this source. They receive a mean Attention Score of 3.8. This one has done well, scoring higher than 75% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 331,040 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 57 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.