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Modeling Within-Host Dynamics of Influenza Virus Infection Including Immune Responses

Overview of attention for article published in PLoS Computational Biology, June 2012
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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 (90th percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

Mentioned by

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1 news outlet
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6 X users
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2 Facebook pages

Citations

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232 Dimensions

Readers on

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220 Mendeley
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Title
Modeling Within-Host Dynamics of Influenza Virus Infection Including Immune Responses
Published in
PLoS Computational Biology, June 2012
DOI 10.1371/journal.pcbi.1002588
Pubmed ID
Authors

Kasia A. Pawelek, Giao T. Huynh, Michelle Quinlivan, Ann Cullinane, Libin Rong, Alan S. Perelson

Abstract

Influenza virus infection remains a public health problem worldwide. The mechanisms underlying viral control during an uncomplicated influenza virus infection are not fully understood. Here, we developed a mathematical model including both innate and adaptive immune responses to study the within-host dynamics of equine influenza virus infection in horses. By comparing modeling predictions with both interferon and viral kinetic data, we examined the relative roles of target cell availability, and innate and adaptive immune responses in controlling the virus. Our results show that the rapid and substantial viral decline (about 2 to 4 logs within 1 day) after the peak can be explained by the killing of infected cells mediated by interferon activated cells, such as natural killer cells, during the innate immune response. After the viral load declines to a lower level, the loss of interferon-induced antiviral effect and an increased availability of target cells due to loss of the antiviral state can explain the observed short phase of viral plateau in which the viral level remains unchanged or even experiences a minor second peak in some animals. An adaptive immune response is needed in our model to explain the eventual viral clearance. This study provides a quantitative understanding of the biological factors that can explain the viral and interferon kinetics during a typical influenza virus infection.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 220 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 3%
Italy 2 <1%
Australia 1 <1%
Germany 1 <1%
India 1 <1%
Brazil 1 <1%
Belgium 1 <1%
United Kingdom 1 <1%
Unknown 206 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 55 25%
Researcher 47 21%
Professor > Associate Professor 16 7%
Student > Bachelor 16 7%
Student > Master 15 7%
Other 37 17%
Unknown 34 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 63 29%
Mathematics 21 10%
Immunology and Microbiology 19 9%
Biochemistry, Genetics and Molecular Biology 15 7%
Medicine and Dentistry 15 7%
Other 46 21%
Unknown 41 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 30 January 2014.
All research outputs
#2,762,423
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#2,488
of 8,964 outputs
Outputs of similar age
#17,307
of 177,656 outputs
Outputs of similar age from PLoS Computational Biology
#21
of 108 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 72% 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 177,656 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 90% of its contemporaries.
We're also able to compare this research output to 108 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.