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Transcriptomic signatures differentiate survival from fatal outcomes in humans infected with Ebola virus

Overview of attention for article published in Genome Biology (Online Edition), January 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#20 of 3,993)
  • High Attention Score compared to outputs of the same age (99th percentile)

Mentioned by

news
42 news outlets
blogs
2 blogs
twitter
38 tweeters
googleplus
1 Google+ user
reddit
1 Redditor

Citations

dimensions_citation
95 Dimensions

Readers on

mendeley
143 Mendeley
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Title
Transcriptomic signatures differentiate survival from fatal outcomes in humans infected with Ebola virus
Published in
Genome Biology (Online Edition), January 2017
DOI 10.1186/s13059-016-1137-3
Pubmed ID
Authors

Xuan Liu, Emily Speranza, César Muñoz-Fontela, Sam Haldenby, Natasha Y. Rickett, Isabel Garcia-Dorival, Yongxiang Fang, Yper Hall, Elsa-Gayle Zekeng, Anja Lüdtke, Dong Xia, Romy Kerber, Ralf Krumkamp, Sophie Duraffour, Daouda Sissoko, John Kenny, Nichola Rockliffe, E. Diane Williamson, Thomas R. Laws, Magassouba N’Faly, David A. Matthews, Stephan Günther, Andrew R. Cossins, Armand Sprecher, John H. Connor, Miles W. Carroll, Julian A. Hiscox

Abstract

In 2014, Western Africa experienced an unanticipated explosion of Ebola virus infections. What distinguishes fatal from non-fatal outcomes remains largely unknown, yet is key to optimising personalised treatment strategies. We used transcriptome data for peripheral blood taken from infected and convalescent recovering patients to identify early stage host factors that are associated with acute illness and those that differentiate patient survival from fatality. The data demonstrate that individuals who succumbed to the disease show stronger upregulation of interferon signalling and acute phase responses compared to survivors during the acute phase of infection. Particularly notable is the strong upregulation of albumin and fibrinogen genes, which suggest significant liver pathology. Cell subtype prediction using messenger RNA expression patterns indicated that NK-cell populations increase in patients who survive infection. By selecting genes whose expression properties discriminated between fatal cases and survivors, we identify a small panel of responding genes that act as strong predictors of patient outcome, independent of viral load. Transcriptomic analysis of the host response to pathogen infection using blood samples taken during an outbreak situation can provide multiple levels of information on both disease state and mechanisms of pathogenesis. Host biomarkers were identified that provide high predictive value under conditions where other predictors, such as viral load, are poor prognostic indicators. The data suggested that rapid analysis of the host response to infection in an outbreak situation can provide valuable information to guide an understanding of disease outcome and mechanisms of disease.

Twitter Demographics

The data shown below were collected from the profiles of 38 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 143 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
United Kingdom 2 1%
Unknown 138 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 31 22%
Student > Ph. D. Student 22 15%
Student > Bachelor 16 11%
Student > Master 15 10%
Other 8 6%
Other 29 20%
Unknown 22 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 26 18%
Agricultural and Biological Sciences 21 15%
Immunology and Microbiology 21 15%
Medicine and Dentistry 17 12%
Computer Science 4 3%
Other 24 17%
Unknown 30 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 348. 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 March 2018.
All research outputs
#68,778
of 21,650,873 outputs
Outputs from Genome Biology (Online Edition)
#20
of 3,993 outputs
Outputs of similar age
#2,145
of 393,013 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 21,650,873 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,993 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has done particularly well, scoring higher than 99% 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 393,013 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them