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Identification of Differentially Expressed Genes in Kawasaki Disease Patients as Potential Biomarkers for IVIG Sensitivity by Bioinformatics Analysis

Overview of attention for article published in Pediatric Cardiology, May 2016
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

twitter
2 tweeters

Readers on

mendeley
8 Mendeley
Title
Identification of Differentially Expressed Genes in Kawasaki Disease Patients as Potential Biomarkers for IVIG Sensitivity by Bioinformatics Analysis
Published in
Pediatric Cardiology, May 2016
DOI 10.1007/s00246-016-1381-z
Pubmed ID
Authors

Lan He, Youyu Sheng, Chunyun Huang, Guoying Huang

Abstract

Kawasaki disease (KD) is a leading cause of acquired heart disease predominantly affecting infants and young children. Intravenous immunoglobulin (IVIG) is applied as the most favorable treatment against KD, but IVIG resistant remains exist. Although several clinical scoring systems have been developed to identify children at highest risk of IVIG resistance, there is a need to identify sufficiently sensitive biomarkers for IVIG treatment. Some differentially expressed genes (DEGs) could be the promising potential biomarkers for IVIG-related sensitivity diagnosis. We employed a systematic and integrative bioinformatics framework to identify such kind of genes. The performance of the candidate genes was evaluated by hierarchical clustering, ROC analysis and literature mining. By analyzing three datasets of KD patients, 34 DEGs of the three groups have been found to be associated with IVIG-related sensitivity. A module of 12 genes could predict resistant group patients with high accuracy, and a module of ten genes could predict responsive group patients effectively with accuracy of 96 %. And three of them are most likely to serve as drug targets or diagnostic biomarkers in the future. Compared with unsupervised hierarchical clustering analysis, our modules could distinct IVIG-resistant patients efficiently. Two groups of DEGs could predict IVIG-related sensitivity with high accuracy, which are potential biomarkers for the clinical diagnosis and prediction of IVIG treatment response in KD patients, improving the prognosis of patients.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Other 2 25%
Student > Bachelor 2 25%
Lecturer > Senior Lecturer 1 13%
Student > Ph. D. Student 1 13%
Researcher 1 13%
Other 1 13%
Readers by discipline Count As %
Medicine and Dentistry 3 38%
Immunology and Microbiology 3 38%
Biochemistry, Genetics and Molecular Biology 1 13%
Unspecified 1 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 13 May 2016.
All research outputs
#7,469,473
of 13,494,439 outputs
Outputs from Pediatric Cardiology
#252
of 1,170 outputs
Outputs of similar age
#113,060
of 262,151 outputs
Outputs of similar age from Pediatric Cardiology
#3
of 36 outputs
Altmetric has tracked 13,494,439 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,170 research outputs from this source. They receive a mean Attention Score of 1.4. This one has done well, scoring higher than 77% 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 262,151 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.