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Added Value of Next-Generation Sequencing for Multilocus Sequence Typing Analysis of aPneumocystis jiroveciiPneumonia Outbreak1

Overview of attention for article published in Emerging Infectious Diseases, August 2017
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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 (84th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

Mentioned by

policy
1 policy source
twitter
17 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
12 Dimensions

Readers on

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19 Mendeley
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Title
Added Value of Next-Generation Sequencing for Multilocus Sequence Typing Analysis of aPneumocystis jiroveciiPneumonia Outbreak1
Published in
Emerging Infectious Diseases, August 2017
DOI 10.3201/eid2308.161295
Pubmed ID
Authors

Elena Charpentier, Cécile Garnaud, Claire Wintenberger, Sébastien Bailly, Jean-Benjamin Murat, John Rendu, Patricia Pavese, Thibault Drouet, Caroline Augier, Paolo Malvezzi, Anne Thiébaut-Bertrand, Marie-Reine Mallaret, Olivier Epaulard, Muriel Cornet, Sylvie Larrat, Danièle Maubon

Abstract

Pneumocystis jirovecii is a major threat for immunocompromised patients, and clusters of pneumocystis pneumonia (PCP) have been increasingly described in transplant units during the past decade. Exploring an outbreak transmission network requires complementary spatiotemporal and strain-typing approaches. We analyzed a PCP outbreak and demonstrated the added value of next-generation sequencing (NGS) for the multilocus sequence typing (MLST) study of P. jirovecii strains. Thirty-two PCP patients were included. Among the 12 solid organ transplant patients, 5 shared a major and unique genotype that was also found as a minor strain in a sixth patient. A transmission map analysis strengthened the suspicion of nosocomial acquisition of this strain for the 6 patients. NGS-MLST enables accurate determination of subpopulation, which allowed excluding other patients from the transmission network. NGS-MLST genotyping approach was essential to deciphering this outbreak. This innovative approach brings new insights for future epidemiologic studies on this uncultivable opportunistic fungus.

Twitter Demographics

The data shown below were collected from the profiles of 17 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 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Other 5 26%
Researcher 3 16%
Student > Ph. D. Student 2 11%
Student > Postgraduate 2 11%
Student > Doctoral Student 2 11%
Other 2 11%
Unknown 3 16%
Readers by discipline Count As %
Medicine and Dentistry 6 32%
Biochemistry, Genetics and Molecular Biology 3 16%
Agricultural and Biological Sciences 3 16%
Economics, Econometrics and Finance 1 5%
Immunology and Microbiology 1 5%
Other 0 0%
Unknown 5 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 20 July 2017.
All research outputs
#1,651,010
of 15,488,537 outputs
Outputs from Emerging Infectious Diseases
#1,897
of 7,474 outputs
Outputs of similar age
#42,553
of 268,925 outputs
Outputs of similar age from Emerging Infectious Diseases
#45
of 117 outputs
Altmetric has tracked 15,488,537 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 7,474 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 29.9. This one has gotten more attention than average, scoring higher than 74% 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 268,925 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 117 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 61% of its contemporaries.