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Exploration of the Fecal Microbiota and Biomarker Discovery in Equine Grass Sickness

Overview of attention for article published in Journal of Proteome Research, February 2018
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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 (88th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

blogs
1 blog
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14 X users

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82 Mendeley
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Article details
Title
Exploration of the Fecal Microbiota and Biomarker Discovery in Equine Grass Sickness
Published in
Journal of Proteome Research, February 2018
DOI 10.1021/acs.jproteome.7b00784
Pubmed ID
Authors
Abstract

Equine grass sickness (EGS) is a frequently fatal disease of horses, responsible for the death of 1-2% of the UK horse population annually. The etiology of this disease is currently uncharacterized although there is evidence it is associated with Clostridium botulinum neurotoxin in the gut. Prevention is currently not possible and ileal biopsy diagnosis is invasive. The aim of this study was to characterize the fecal microbiota and biofluid metabolic profiles of EGS horses, to further understand the mechanisms underlying this disease and identify metabolic biomarkers to aid in diagnosis. Urine, plasma and feces were collected from horses with EGS, matched controls (MC), and hospital controls (HC). Sequencing the16S rRNA gene of the fecal bacterial population of the study horses found a severe dysbiosis in EGS horses, with an increase in Bacteroidetes and a decrease in Firmicutes bacteria. Metabolic profiling by 1H nuclear magnetic resonance (NMR) spectroscopy found EGS to be associated with the lower urinary excretion of hippurate and 4-cresyl sulfate and higher excretion of O-acetyl carnitine and trimethylamine-N-oxide (TMAO). The predictive ability of the complete urinary metabolic signature and using the four discriminatory urinary metabolites to classify horses by disease status was assessed using a second (test) set of horses. The urinary metabolome and a combination of the four candidate biomarkers showed promise in aiding the identification of horses with EGS. Characterization of the metabolic shifts associated with EGS offers the potential of a non-invasive test to aid pre-mortem diagnosis.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 14 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 82 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Professor > Associate Professor 12 15%
Researcher 10 12%
Professor 6 7%
Student > Master 6 7%
Student > Doctoral Student 5 6%
Other 19 23%
Unknown 24 29%
Readers by discipline
Readers by discipline Count As %
Veterinary Science and Veterinary Medicine 21 26%
Agricultural and Biological Sciences 9 11%
Energy 9 11%
Biochemistry, Genetics and Molecular Biology 5 6%
Medicine and Dentistry 3 4%
Other 8 10%
Unknown 27 33%
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 18 September 2018.
All research outputs
#2,717,039
of 28,829,220 outputs
Outputs from Journal of Proteome Research
#528
of 7,019 outputs
Outputs of similar age
#53,402
of 458,701 outputs
Outputs of similar age from Journal of Proteome Research
#13
of 92 outputs
Altmetric has tracked 28,829,220 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 7,019 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.7. This one has done particularly well, scoring higher than 92% 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 458,701 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 88% of its contemporaries.
We're also able to compare this research output to 92 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.