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Applying Mathematical Tools to Accelerate Vaccine Development: Modeling Shigella Immune Dynamics

Overview of attention for article published in PLOS ONE, April 2013
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

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2 news outlets
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3 X users
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1 Facebook page

Citations

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6 Dimensions

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38 Mendeley
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Title
Applying Mathematical Tools to Accelerate Vaccine Development: Modeling Shigella Immune Dynamics
Published in
PLOS ONE, April 2013
DOI 10.1371/journal.pone.0059465
Pubmed ID
Authors

Courtney L. Davis, Rezwanul Wahid, Franklin R. Toapanta, Jakub K. Simon, Marcelo B. Sztein, Doron Levy

Abstract

We establish a mathematical framework for studying immune interactions with Shigella, a bacteria that kills over one million people worldwide every year. The long-term goal of this novel approach is to inform Shigella vaccine design by elucidating which immune components and bacterial targets are crucial for establishing Shigella immunity. Our delay differential equation model focuses on antibody and B cell responses directed against antigens like lipopolysaccharide in Shigella's outer membrane. We find that antibody-based vaccines targeting only surface antigens cannot elicit sufficient immunity for protection. Additional boosting prior to infection would require a four-orders-of-magnitude increase in antibodies to sufficiently prevent epithelial invasion. However, boosting anti-LPS B memory can confer protection, which suggests these cells may correlate with immunity. We see that IgA antibodies are slightly more effective per molecule than IgG, but more total IgA is required due to spatial functionality. An extension of the model reveals that targeting both LPS and epithelial entry proteins is a promising avenue to advance vaccine development. This paper underscores the importance of multifaceted immune targeting in creating an effective Shigella vaccine. It introduces mathematical models to the Shigella vaccine development effort and lays a foundation for joint theoretical/experimental/clinical approaches to Shigella vaccine design.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Uruguay 1 3%
Germany 1 3%
Unknown 35 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 26%
Student > Ph. D. Student 6 16%
Student > Master 5 13%
Professor 2 5%
Student > Bachelor 2 5%
Other 8 21%
Unknown 5 13%
Readers by discipline Count As %
Mathematics 5 13%
Medicine and Dentistry 5 13%
Agricultural and Biological Sciences 4 11%
Immunology and Microbiology 4 11%
Biochemistry, Genetics and Molecular Biology 3 8%
Other 9 24%
Unknown 8 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 20. 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 05 January 2018.
All research outputs
#1,651,299
of 23,314,015 outputs
Outputs from PLOS ONE
#21,146
of 199,281 outputs
Outputs of similar age
#13,832
of 201,245 outputs
Outputs of similar age from PLOS ONE
#490
of 5,295 outputs
Altmetric has tracked 23,314,015 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 199,281 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.3. This one has done well, scoring higher than 89% 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 201,245 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 5,295 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 90% of its contemporaries.