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Next-generation sequencing diagnostics of bacteremia in septic patients

Overview of attention for article published in Genome Medicine, July 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 (97th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

news
8 news outlets
blogs
1 blog
twitter
31 X users
patent
6 patents
facebook
2 Facebook pages

Citations

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270 Dimensions

Readers on

mendeley
358 Mendeley
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Title
Next-generation sequencing diagnostics of bacteremia in septic patients
Published in
Genome Medicine, July 2016
DOI 10.1186/s13073-016-0326-8
Pubmed ID
Authors

Silke Grumaz, Philip Stevens, Christian Grumaz, Sebastian O. Decker, Markus A. Weigand, Stefan Hofer, Thorsten Brenner, Arndt von Haeseler, Kai Sohn

Abstract

Bloodstream infections remain one of the major challenges in intensive care units, leading to sepsis or even septic shock in many cases. Due to the lack of timely diagnostic approaches with sufficient sensitivity, mortality rates of sepsis are still unacceptably high. However a prompt diagnosis of the causative microorganism is critical to significantly improve outcome of bloodstream infections. Although various targeted molecular tests for blood samples are available, time-consuming blood culture-based approaches still represent the standard of care for the identification of bacteria. Here we describe the establishment of a complete diagnostic workflow for the identification of infectious microorganisms from seven septic patients based on unbiased sequence analyses of free circulating DNA from plasma by next-generation sequencing. We found significant levels of DNA fragments derived from pathogenic bacteria in samples from septic patients. Quantitative evaluation of normalized read counts and introduction of a sepsis indicating quantifier (SIQ) score allowed for an unambiguous identification of Gram-positive as well as Gram-negative bacteria that exactly matched with blood cultures from corresponding patient samples. In addition, we also identified species from samples where blood cultures were negative. Reads of non-human origin also comprised fragments derived from antimicrobial resistance genes, showing that, in principle, prediction of specific types of resistance might be possible. The complete workflow from sample preparation to species identification report could be accomplished in roughly 30 h, thus making this approach a promising diagnostic platform for critically ill patients suffering from bloodstream infections.

X Demographics

X Demographics

The data shown below were collected from the profiles of 31 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 358 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 <1%
Israel 1 <1%
United States 1 <1%
Unknown 354 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 65 18%
Student > Ph. D. Student 56 16%
Student > Bachelor 42 12%
Other 38 11%
Student > Master 38 11%
Other 49 14%
Unknown 70 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 74 21%
Agricultural and Biological Sciences 57 16%
Medicine and Dentistry 52 15%
Immunology and Microbiology 29 8%
Engineering 11 3%
Other 40 11%
Unknown 95 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 93. 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 December 2022.
All research outputs
#458,669
of 25,452,734 outputs
Outputs from Genome Medicine
#77
of 1,590 outputs
Outputs of similar age
#9,143
of 367,374 outputs
Outputs of similar age from Genome Medicine
#6
of 29 outputs
Altmetric has tracked 25,452,734 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,590 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.7. 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 367,374 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 29 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.