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Mechanisms of Severe Acute Respiratory Syndrome Coronavirus-Induced Acute Lung Injury

Overview of attention for article published in mBio, August 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

twitter
33 tweeters
googleplus
1 Google+ user

Citations

dimensions_citation
112 Dimensions

Readers on

mendeley
148 Mendeley
citeulike
1 CiteULike
Title
Mechanisms of Severe Acute Respiratory Syndrome Coronavirus-Induced Acute Lung Injury
Published in
mBio, August 2013
DOI 10.1128/mbio.00271-13
Pubmed ID
Authors

Lisa E. Gralinski, Armand Bankhead, Sophia Jeng, Vineet D. Menachery, Sean Proll, Sarah E. Belisle, Melissa Matzke, Bobbie-Jo M. Webb-Robertson, Maria L. Luna, Anil K. Shukla, Martin T. Ferris, Meagan Bolles, Jean Chang, Lauri Aicher, Katrina M. Waters, Richard D. Smith, Thomas O. Metz, G. Lynn Law, Michael G. Katze, Shannon McWeeney, Ralph S. Baric

Abstract

Systems biology offers considerable promise in uncovering novel pathways by which viruses and other microbial pathogens interact with host signaling and expression networks to mediate disease severity. In this study, we have developed an unbiased modeling approach to identify new pathways and network connections mediating acute lung injury, using severe acute respiratory syndrome coronavirus (SARS-CoV) as a model pathogen. We utilized a time course of matched virologic, pathological, and transcriptomic data within a novel methodological framework that can detect pathway enrichment among key highly connected network genes. This unbiased approach produced a high-priority list of 4 genes in one pathway out of over 3,500 genes that were differentially expressed following SARS-CoV infection. With these data, we predicted that the urokinase and other wound repair pathways would regulate lethal versus sublethal disease following SARS-CoV infection in mice. We validated the importance of the urokinase pathway for SARS-CoV disease severity using genetically defined knockout mice, proteomic correlates of pathway activation, and pathological disease severity. The results of these studies demonstrate that a fine balance exists between host coagulation and fibrinolysin pathways regulating pathological disease outcomes, including diffuse alveolar damage and acute lung injury, following infection with highly pathogenic respiratory viruses, such as SARS-CoV.

Twitter Demographics

The data shown below were collected from the profiles of 33 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 148 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
Brazil 1 <1%
Unknown 143 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 31 21%
Student > Ph. D. Student 25 17%
Student > Master 20 14%
Student > Bachelor 15 10%
Student > Postgraduate 8 5%
Other 29 20%
Unknown 20 14%
Readers by discipline Count As %
Medicine and Dentistry 41 28%
Biochemistry, Genetics and Molecular Biology 23 16%
Agricultural and Biological Sciences 17 11%
Immunology and Microbiology 10 7%
Engineering 5 3%
Other 21 14%
Unknown 31 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 21. 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 01 July 2020.
All research outputs
#971,811
of 15,861,892 outputs
Outputs from mBio
#910
of 3,999 outputs
Outputs of similar age
#10,917
of 160,422 outputs
Outputs of similar age from mBio
#19
of 65 outputs
Altmetric has tracked 15,861,892 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,999 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 24.8. 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 160,422 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 93% of its contemporaries.
We're also able to compare this research output to 65 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 70% of its contemporaries.