↓ Skip to main content

Gene Expression-Based Classifiers Identify Staphylococcus aureus Infection in Mice and Humans

Overview of attention for article published in PLoS ONE, January 2013
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

news
1 news outlet
blogs
3 blogs
twitter
1 tweeter
patent
1 patent

Citations

dimensions_citation
32 Dimensions

Readers on

mendeley
57 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Gene Expression-Based Classifiers Identify Staphylococcus aureus Infection in Mice and Humans
Published in
PLoS ONE, January 2013
DOI 10.1371/journal.pone.0048979
Pubmed ID
Authors

Sun Hee Ahn, Ephraim L. Tsalik, Derek D. Cyr, Yurong Zhang, Jennifer C. van Velkinburgh, Raymond J. Langley, Seth W. Glickman, Charles B. Cairns, Aimee K. Zaas, Emanuel P. Rivers, Ronny M. Otero, Tim Veldman, Stephen F. Kingsmore, Joseph Lucas, Christopher W. Woods, Geoffrey S. Ginsburg, Vance G. Fowler

Abstract

Staphylococcus aureus causes a spectrum of human infection. Diagnostic delays and uncertainty lead to treatment delays and inappropriate antibiotic use. A growing literature suggests the host's inflammatory response to the pathogen represents a potential tool to improve upon current diagnostics. The hypothesis of this study is that the host responds differently to S. aureus than to E. coli infection in a quantifiable way, providing a new diagnostic avenue. This study uses Bayesian sparse factor modeling and penalized binary regression to define peripheral blood gene-expression classifiers of murine and human S. aureus infection. The murine-derived classifier distinguished S. aureus infection from healthy controls and Escherichia coli-infected mice across a range of conditions (mouse and bacterial strain, time post infection) and was validated in outbred mice (AUC>0.97). A S. aureus classifier derived from a cohort of 94 human subjects distinguished S. aureus blood stream infection (BSI) from healthy subjects (AUC 0.99) and E. coli BSI (AUC 0.84). Murine and human responses to S. aureus infection share common biological pathways, allowing the murine model to classify S. aureus BSI in humans (AUC 0.84). Both murine and human S. aureus classifiers were validated in an independent human cohort (AUC 0.95 and 0.92, respectively). The approach described here lends insight into the conserved and disparate pathways utilized by mice and humans in response to these infections. Furthermore, this study advances our understanding of S. aureus infection; the host response to it; and identifies new diagnostic and therapeutic avenues.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Norway 1 2%
Taiwan 1 2%
Switzerland 1 2%
Unknown 53 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 33%
Student > Ph. D. Student 11 19%
Student > Master 5 9%
Student > Doctoral Student 5 9%
Other 3 5%
Other 10 18%
Unknown 4 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 33%
Medicine and Dentistry 11 19%
Immunology and Microbiology 8 14%
Biochemistry, Genetics and Molecular Biology 6 11%
Unspecified 2 4%
Other 5 9%
Unknown 6 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 35. 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 26 December 2017.
All research outputs
#405,096
of 12,348,212 outputs
Outputs from PLoS ONE
#7,940
of 134,791 outputs
Outputs of similar age
#9,434
of 279,198 outputs
Outputs of similar age from PLoS ONE
#306
of 6,464 outputs
Altmetric has tracked 12,348,212 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 134,791 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.7. This one has done particularly well, scoring higher than 94% 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 279,198 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 96% of its contemporaries.
We're also able to compare this research output to 6,464 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 95% of its contemporaries.