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A statistical analysis of the effects of urease pre-treatment on the measurement of the urinary metabolome by gas chromatography–mass spectrometry

Overview of attention for article published in Metabolomics, February 2014
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Title
A statistical analysis of the effects of urease pre-treatment on the measurement of the urinary metabolome by gas chromatography–mass spectrometry
Published in
Metabolomics, February 2014
DOI 10.1007/s11306-014-0642-1
Pubmed ID
Authors

Bobbie-Jo Webb-Robertson, Young-Mo Kim, Erika M. Zink, Katherine A. Hallaian, Qibin Zhang, Ramana Madupu, Katrina M. Waters, Thomas O. Metz

Abstract

Urease pre-treatment of urine has been utilized since the early 1960s to remove high levels of urea from samples prior to further processing and analysis by gas chromatography-mass spectrometry (GC-MS). Aside from the obvious depletion or elimination of urea, the effect, if any, of urease pre-treatment on the urinary metabolome has not been studied in detail. Here, we report the results of three separate but related experiments that were designed to assess possible indirect effects of urease pre-treatment on the urinary metabolome as measured by GC-MS. In total, 235 GC-MS analyses were performed and over 106 identified and 200 unidentified metabolites were quantified across the three experiments. The results showed that data from urease pre-treated samples 1) had the same or lower coefficients of variance among reproducibly detected metabolites, 2) more accurately reflected quantitative differences and the expected ratios among different urine volumes, and 3) increased the number of metabolite identifications. Overall, we observed no negative consequences of urease pre-treatment. In contrast, urease pretreatment enhanced the ability to distinguish between volume-based and biological sample types compared to no treatment. Taken together, these results show that urease pretreatment of urine offers multiple beneficial effects that outweigh any artifacts that may be introduced to the data in urinary metabolomics analyses.

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Mendeley readers

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Geographical breakdown

Country Count As %
Unknown 48 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 19%
Student > Ph. D. Student 8 17%
Student > Master 6 13%
Student > Bachelor 4 8%
Other 3 6%
Other 8 17%
Unknown 10 21%
Readers by discipline Count As %
Chemistry 11 23%
Agricultural and Biological Sciences 9 19%
Biochemistry, Genetics and Molecular Biology 8 17%
Medicine and Dentistry 2 4%
Environmental Science 1 2%
Other 4 8%
Unknown 13 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 15 September 2014.
All research outputs
#17,726,563
of 22,763,032 outputs
Outputs from Metabolomics
#1,010
of 1,292 outputs
Outputs of similar age
#153,733
of 221,194 outputs
Outputs of similar age from Metabolomics
#19
of 28 outputs
Altmetric has tracked 22,763,032 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,292 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 17th percentile – i.e., 17% of its peers scored the same or lower than it.
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We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.