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Dynamics of the fecal microbiome in patients with recurrent and nonrecurrent Clostridium difficile infection

Overview of attention for article published in Genome Medicine, April 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • Average Attention Score compared to outputs of the same age and source

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1 blog
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12 X users

Citations

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102 Dimensions

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137 Mendeley
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Title
Dynamics of the fecal microbiome in patients with recurrent and nonrecurrent Clostridium difficile infection
Published in
Genome Medicine, April 2016
DOI 10.1186/s13073-016-0298-8
Pubmed ID
Authors

Anna Maria Seekatz, Krishna Rao, Kavitha Santhosh, Vincent Bensan Young

Abstract

Recurrent Clostridium difficile infection (CDI) remains problematic, with up to 30 % of individuals diagnosed with primary CDI experiencing at least one episode of recurrence. The success of microbial-based therapeutics, such as fecal microbiota transplantation, for the treatment of recurrent CDI underscores the importance of restoring the microbiota. However, few studies have looked at the microbial factors that contribute to the development of recurrent disease. Here we compare microbial changes over time in patients with or without recurrence to identify microbial signatures associated with the development of recurrence. We used 16S rRNA-encoding gene sequence analysis to compare the fecal microbiota of 93 patients with recurrent and nonrecurrent CDI, sampled longitudinally. Cross-group and intra-individual differences in microbial community diversity and similarity were compared prior to the development of recurrent disease and over time. Samples from these patient groups exhibited variable community profiles, clustering into four distinct community groups. Cross-group comparison of the index sample collected from patients that did or did not develop recurrence revealed differences in diversity and community structure (analysis of molecular variance, p < 0.05). Intra-individual comparisons of the microbiota were more informative and samples from recurrent patients were less likely to recover in diversity (Chi-square test, p < 0.005), exhibiting less community similarity overall (Kruskal-Wallis test, p < 0.05). Interestingly, patients with severe disease harbored a significantly less diverse community, a trend that was observed across both nonrecurrent and recurrent patient groups (Wilcoxon test, p < 0.05). To date, this study represents one of the largest studies focused on the relationship between predictive signals from the gut microbiota and the development of recurrent CDI. Our data demonstrate that specific microbiota-derived characteristics associate with disease severity and recurrence and that future studies could incorporate these characteristics into predictive models.

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The data shown below were collected from the profiles of 12 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 137 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
China 1 <1%
Brazil 1 <1%
Unknown 134 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 32 23%
Student > Ph. D. Student 25 18%
Student > Master 13 9%
Student > Bachelor 12 9%
Student > Postgraduate 6 4%
Other 23 17%
Unknown 26 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 21%
Biochemistry, Genetics and Molecular Biology 22 16%
Medicine and Dentistry 20 15%
Immunology and Microbiology 13 9%
Computer Science 5 4%
Other 21 15%
Unknown 27 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 29 April 2016.
All research outputs
#2,412,812
of 24,885,505 outputs
Outputs from Genome Medicine
#548
of 1,532 outputs
Outputs of similar age
#37,810
of 304,728 outputs
Outputs of similar age from Genome Medicine
#19
of 35 outputs
Altmetric has tracked 24,885,505 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,532 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.2. This one has gotten more attention than average, scoring higher than 64% 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 304,728 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 35 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.