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In Silico Prediction and Experimental Confirmation of HA Residues Conferring Enhanced Human Receptor Specificity of H5N1 Influenza A Viruses

Overview of attention for article published in Scientific Reports, June 2015
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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 (97th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

news
8 news outlets
twitter
5 X users
facebook
1 Facebook page

Citations

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40 Dimensions

Readers on

mendeley
47 Mendeley
Title
In Silico Prediction and Experimental Confirmation of HA Residues Conferring Enhanced Human Receptor Specificity of H5N1 Influenza A Viruses
Published in
Scientific Reports, June 2015
DOI 10.1038/srep11434
Pubmed ID
Authors

Sonja Schmier, Ahmed Mostafa, Thomas Haarmann, Norbert Bannert, John Ziebuhr, Veljko Veljkovic, Ursula Dietrich, Stephan Pleschka

Abstract

Newly emerging influenza A viruses (IAV) pose a major threat to human health by causing seasonal epidemics and/or pandemics, the latter often facilitated by the lack of pre-existing immunity in the general population. Early recognition of candidate pandemic influenza viruses (CPIV) is of crucial importance for restricting virus transmission and developing appropriate therapeutic and prophylactic strategies including effective vaccines. Often, the pandemic potential of newly emerging IAV is only fully recognized once the virus starts to spread efficiently causing serious disease in humans. Here, we used a novel phylogenetic algorithm based on the informational spectrum method (ISM) to identify potential CPIV by predicting mutations in the viral hemagglutinin (HA) gene that are likely to (differentially) affect critical interactions between the HA protein and target cells from bird and human origin, respectively. Predictions were subsequently validated by generating pseudotyped retrovirus particles and genetically engineered IAV containing these mutations and characterizing potential effects on virus entry and replication in cells expressing human and avian IAV receptors, respectively. Our data suggest that the ISM-based algorithm is suitable to identify CPIV among IAV strains that are circulating in animal hosts and thus may be a new tool for assessing pandemic risks associated with specific strains.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 1 2%
Unknown 46 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 23%
Student > Ph. D. Student 7 15%
Student > Master 6 13%
Student > Bachelor 4 9%
Student > Doctoral Student 2 4%
Other 9 19%
Unknown 8 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 30%
Medicine and Dentistry 6 13%
Biochemistry, Genetics and Molecular Biology 6 13%
Veterinary Science and Veterinary Medicine 3 6%
Immunology and Microbiology 3 6%
Other 7 15%
Unknown 8 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 66. 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 February 2018.
All research outputs
#545,038
of 22,813,792 outputs
Outputs from Scientific Reports
#6,102
of 123,141 outputs
Outputs of similar age
#6,637
of 264,785 outputs
Outputs of similar age from Scientific Reports
#76
of 2,014 outputs
Altmetric has tracked 22,813,792 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 123,141 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.2. This one has done particularly well, scoring higher than 95% 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 264,785 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 97% of its contemporaries.
We're also able to compare this research output to 2,014 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 96% of its contemporaries.