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Diagnosing inflammation and infection in the urinary system via proteomics

Overview of attention for article published in Journal of Translational Medicine, April 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

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1 blog
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3 X users
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1 patent
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1 Facebook page

Citations

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47 Dimensions

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84 Mendeley
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Title
Diagnosing inflammation and infection in the urinary system via proteomics
Published in
Journal of Translational Medicine, April 2015
DOI 10.1186/s12967-015-0475-3
Pubmed ID
Authors

Yanbao Yu, Patricia Sikorski, Cynthia Bowman-Gholston, Nicolas Cacciabeve, Karen E Nelson, Rembert Pieper

Abstract

Current methodology for the diagnosis of diseases in the urinary system includes patient symptomology, urine analysis and urine culture. Asymptomatic bacteriuria from urethral colonization or indwelling catheters, sample contamination from perineal or vaginal sources, and non-infectious inflammatory conditions can mimic UTIs, leading to uncertainty on medical treatment decisions. Innovative shotgun metaproteomic methods were used to analyze urine sediments from 120 patients also subjected to conventional urinalysis for various clinical reasons including suspected UTIs. The proteomic data were simultaneously searched for the presence of microbial agents, inflammation, immune responses against pathogens, and evidence of urothelial tissue injury. Hierarchical clustering analysis was performed to identify host protein patterns discerning UTI from urethral colonization and vaginal contamination of urine samples. Organisms causing more than 98% of all UTIs and commensal microbes of the urogenital and perineal area were identified from 76 urine sediments with detection sensitivities estimated to be similar to urine culture. Proteomic data permitted a thorough evaluation of inflammatory and antimicrobial immune responses. Hierarchical clustering of the data revealed that high abundances of proteins from activated neutrophils were associated with pathogens in most cases, and correlated well with leukocyte esterase activities and leukocyte counts via microscopy. Proteomic data also allowed assessments of urothelial injury, by quantifying proteins highly expressed in red blood cells and contributing to the acute phase response. Lactobacillus and Gardnerella vaginalis were frequently identified suggesting urethral colonization and/or vaginal contamination of urine. A metaproteomic approach of interest for routine urine clinical diagnostics is presented. As compared to urinalysis and urine culture methods, the data are derived from a single experiment for a given sample and provide additional insights into presence or absence of inflammatory responses and vaginal contamination of urine specimens.

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X Demographics

The data shown below were collected from the profiles of 3 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 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 1%
United States 1 1%
Singapore 1 1%
Unknown 81 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 15%
Researcher 11 13%
Student > Bachelor 10 12%
Student > Master 9 11%
Student > Postgraduate 8 10%
Other 18 21%
Unknown 15 18%
Readers by discipline Count As %
Medicine and Dentistry 14 17%
Biochemistry, Genetics and Molecular Biology 13 15%
Agricultural and Biological Sciences 10 12%
Immunology and Microbiology 9 11%
Nursing and Health Professions 4 5%
Other 15 18%
Unknown 19 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 13 January 2022.
All research outputs
#2,419,118
of 22,875,477 outputs
Outputs from Journal of Translational Medicine
#390
of 4,004 outputs
Outputs of similar age
#33,327
of 264,932 outputs
Outputs of similar age from Journal of Translational Medicine
#7
of 86 outputs
Altmetric has tracked 22,875,477 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,004 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one has done particularly well, scoring higher than 90% 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 264,932 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 86 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.