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Using the Ferret as an Animal Model for Investigating Influenza Antiviral Effectiveness

Overview of attention for article published in Frontiers in Microbiology, February 2016
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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 (94th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

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4 news outlets
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3 X users

Readers on

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104 Mendeley
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Title
Using the Ferret as an Animal Model for Investigating Influenza Antiviral Effectiveness
Published in
Frontiers in Microbiology, February 2016
DOI 10.3389/fmicb.2016.00080
Pubmed ID
Authors

Ding Y. Oh, Aeron C. Hurt

Abstract

The concern of the emergence of a pandemic influenza virus has sparked an increased effort toward the development and testing of novel influenza antivirals. Central to this is the animal model of influenza infection, which has played an important role in understanding treatment effectiveness and the effect of antivirals on host immune responses. Among the different animal models of influenza, ferrets can be considered the most suitable for antiviral studies as they display most of the human-like symptoms following influenza infections, they can be infected with human influenza virus without prior viral adaptation and have the ability to transmit influenza virus efficiently between one another. However, an accurate assessment of the effectiveness of an antiviral treatment in ferrets is dependent on three major experimental considerations encompassing firstly, the volume and titer of virus, and the route of viral inoculation. Secondly, the route and dose of drug administration, and lastly, the different methods used to assess clinical symptoms, viral shedding kinetics and host immune responses in the ferrets. A good understanding of these areas is necessary to achieve data that can accurately inform the human use of influenza antivirals. In this review, we discuss the current progress and the challenges faced in these three major areas when using the ferret model to measure influenza antiviral effectiveness.

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 104 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Canada 1 <1%
Unknown 101 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 20%
Student > Ph. D. Student 17 16%
Student > Bachelor 14 13%
Student > Master 9 9%
Other 7 7%
Other 10 10%
Unknown 26 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 19%
Immunology and Microbiology 11 11%
Biochemistry, Genetics and Molecular Biology 11 11%
Medicine and Dentistry 8 8%
Veterinary Science and Veterinary Medicine 5 5%
Other 18 17%
Unknown 31 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 32. 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 22 October 2022.
All research outputs
#1,100,900
of 23,567,572 outputs
Outputs from Frontiers in Microbiology
#611
of 26,073 outputs
Outputs of similar age
#21,112
of 400,208 outputs
Outputs of similar age from Frontiers in Microbiology
#14
of 486 outputs
Altmetric has tracked 23,567,572 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 26,073 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.4. This one has done particularly well, scoring higher than 97% 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 400,208 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 94% of its contemporaries.
We're also able to compare this research output to 486 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 97% of its contemporaries.