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How low can we go? The implications of low bacterial load in respiratory microbiota studies

Overview of attention for article published in Pneumonia, July 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#20 of 121)
  • High Attention Score compared to outputs of the same age (87th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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29 X users

Citations

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38 Dimensions

Readers on

mendeley
77 Mendeley
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Title
How low can we go? The implications of low bacterial load in respiratory microbiota studies
Published in
Pneumonia, July 2018
DOI 10.1186/s41479-018-0051-8
Pubmed ID
Authors

Robyn L. Marsh, Maria T. Nelson, Chris E. Pope, Amanda J. Leach, Lucas R. Hoffman, Anne B. Chang, Heidi C. Smith-Vaughan

Abstract

Culture-independent sequencing methods are increasingly used to investigate the microbiota associated with human mucosal surfaces, including sites that have low bacterial load in healthy individuals (e.g. the lungs). Standard microbiota methods developed for analysis of high bacterial load specimens (e.g. stool) may require modification when bacterial load is low, as background contamination derived from sterile laboratory reagents and kits can dominate sequence data when few bacteria are present. Bacterial load in respiratory specimens may vary depending on the specimen type, specimen volume, the anatomic site sampled and clinical parameters. This review discusses methodological issues inherent to analysis of low bacterial load specimens and recommends strategies for successful respiratory microbiota studies. The range of methods currently used to process DNA from low bacterial load specimens, and the strategies used to identify and exclude background contamination are also discussed. Microbiota studies that include low bacterial load specimens require additional tests to ensure that background contamination does not bias the results or interpretation. Several methods are currently used to analyse the microbiota in low bacterial load respiratory specimens; however, there is scant literature comparing the effectiveness and biases of different methods. Further research is needed to define optimal methods for analysing the microbiota in low bacterial load specimens.

X Demographics

X Demographics

The data shown below were collected from the profiles of 29 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 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 77 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 22%
Student > Ph. D. Student 15 19%
Student > Doctoral Student 7 9%
Student > Bachelor 6 8%
Student > Postgraduate 5 6%
Other 9 12%
Unknown 18 23%
Readers by discipline Count As %
Medicine and Dentistry 12 16%
Immunology and Microbiology 12 16%
Agricultural and Biological Sciences 11 14%
Biochemistry, Genetics and Molecular Biology 10 13%
Engineering 3 4%
Other 6 8%
Unknown 23 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 24 September 2018.
All research outputs
#2,078,603
of 24,580,204 outputs
Outputs from Pneumonia
#20
of 121 outputs
Outputs of similar age
#42,717
of 332,354 outputs
Outputs of similar age from Pneumonia
#4
of 5 outputs
Altmetric has tracked 24,580,204 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 121 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.6. This one has done well, scoring higher than 84% 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 332,354 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 87% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one.