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Microbiota-based model improves the sensitivity of fecal immunochemical test for detecting colonic lesions

Overview of attention for article published in Genome Medicine, April 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 (93rd percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

blogs
1 blog
twitter
35 X users
patent
1 patent
facebook
1 Facebook page
wikipedia
1 Wikipedia page
googleplus
1 Google+ user

Citations

dimensions_citation
271 Dimensions

Readers on

mendeley
309 Mendeley
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Title
Microbiota-based model improves the sensitivity of fecal immunochemical test for detecting colonic lesions
Published in
Genome Medicine, April 2016
DOI 10.1186/s13073-016-0290-3
Pubmed ID
Authors

Nielson T. Baxter, Mack T. Ruffin, Mary A. M. Rogers, Patrick D. Schloss

Abstract

Colorectal cancer (CRC) is the second leading cause of death among cancers in the United States. Although individuals diagnosed early have a greater than 90 % chance of survival, more than one-third of individuals do not adhere to screening recommendations partly because the standard diagnostics, colonoscopy and sigmoidoscopy, are expensive and invasive. Thus, there is a great need to improve the sensitivity of non-invasive tests to detect early stage cancers and adenomas. Numerous studies have identified shifts in the composition of the gut microbiota associated with the progression of CRC, suggesting that the gut microbiota may represent a reservoir of biomarkers that would complement existing non-invasive methods such as the widely used fecal immunochemical test (FIT). We sequenced the 16S rRNA genes from the stool samples of 490 patients. We used the relative abundances of the bacterial populations within each sample to develop a random forest classification model that detects colonic lesions using the relative abundance of gut microbiota and the concentration of hemoglobin in stool. The microbiota-based random forest model detected 91.7 % of cancers and 45.5 % of adenomas while FIT alone detected 75.0 % and 15.7 %, respectively. Of the colonic lesions missed by FIT, the model detected 70.0 % of cancers and 37.7 % of adenomas. We confirmed known associations of Porphyromonas assaccharolytica, Peptostreptococcus stomatis, Parvimonas micra, and Fusobacterium nucleatum with CRC. Yet, we found that the loss of potentially beneficial organisms, such as members of the Lachnospiraceae, was more predictive for identifying patients with adenomas when used in combination with FIT. These findings demonstrate the potential for microbiota analysis to complement existing screening methods to improve detection of colonic lesions.

X Demographics

X Demographics

The data shown below were collected from the profiles of 35 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 309 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 <1%
United States 1 <1%
Unknown 306 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 56 18%
Researcher 50 16%
Student > Master 43 14%
Student > Bachelor 35 11%
Student > Doctoral Student 17 6%
Other 44 14%
Unknown 64 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 61 20%
Biochemistry, Genetics and Molecular Biology 58 19%
Medicine and Dentistry 51 17%
Immunology and Microbiology 27 9%
Computer Science 12 4%
Other 28 9%
Unknown 72 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 34. 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 06 February 2024.
All research outputs
#1,191,488
of 25,452,734 outputs
Outputs from Genome Medicine
#237
of 1,590 outputs
Outputs of similar age
#20,389
of 315,897 outputs
Outputs of similar age from Genome Medicine
#9
of 37 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 95th 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 well, scoring higher than 85% 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 315,897 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 93% of its contemporaries.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.