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Illuminating uveitis: metagenomic deep sequencing identifies common and rare pathogens

Overview of attention for article published in Genome Medicine, August 2016
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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 (90th percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

Mentioned by

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1 news outlet
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13 X users
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1 Facebook page

Citations

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162 Dimensions

Readers on

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133 Mendeley
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Title
Illuminating uveitis: metagenomic deep sequencing identifies common and rare pathogens
Published in
Genome Medicine, August 2016
DOI 10.1186/s13073-016-0344-6
Pubmed ID
Authors

Thuy Doan, Michael R. Wilson, Emily D. Crawford, Eric D. Chow, Lillian M. Khan, Kristeene A. Knopp, Brian D. O’Donovan, Dongxiang Xia, Jill K. Hacker, Jay M. Stewart, John A. Gonzales, Nisha R. Acharya, Joseph L. DeRisi

Abstract

Ocular infections remain a major cause of blindness and morbidity worldwide. While prognosis is dependent on the timing and accuracy of diagnosis, the etiology remains elusive in ~50 % of presumed infectious uveitis cases. The objective of this study is to determine if unbiased metagenomic deep sequencing (MDS) can accurately detect pathogens in intraocular fluid samples of patients with uveitis. This is a proof-of-concept study, in which intraocular fluid samples were obtained from five subjects with known diagnoses, and one subject with bilateral chronic uveitis without a known etiology. Samples were subjected to MDS, and results were compared with those from conventional diagnostic tests. Pathogens were identified using a rapid computational pipeline to analyze the non-host sequences obtained from MDS. Unbiased MDS of intraocular fluid produced results concordant with known diagnoses in subjects with (n = 4) and without (n = 1) uveitis. Samples positive for Cryptococcus neoformans, Toxoplasma gondii, and herpes simplex virus 1 as tested by a Clinical Laboratory Improvement Amendments-certified laboratory were correctly identified with MDS. Rubella virus was identified in one case of chronic bilateral idiopathic uveitis. The subject's strain was most closely related to a German rubella virus strain isolated in 1992, one year before he developed a fever and rash while living in Germany. The pattern and the number of viral identified mutations present in the patient's strain were consistent with long-term viral replication in the eye. MDS can identify fungi, parasites, and DNA and RNA viruses in minute volumes of intraocular fluid samples. The identification of chronic intraocular rubella virus infection highlights the eye's role as a long-term pathogen reservoir, which has implications for virus eradication and emerging global epidemics.

X Demographics

X Demographics

The data shown below were collected from the profiles of 13 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 133 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 2%
United Kingdom 1 <1%
Sweden 1 <1%
Netherlands 1 <1%
Unknown 128 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 33 25%
Student > Ph. D. Student 19 14%
Student > Master 17 13%
Student > Bachelor 12 9%
Student > Postgraduate 9 7%
Other 28 21%
Unknown 15 11%
Readers by discipline Count As %
Medicine and Dentistry 32 24%
Agricultural and Biological Sciences 25 19%
Biochemistry, Genetics and Molecular Biology 20 15%
Immunology and Microbiology 12 9%
Computer Science 5 4%
Other 16 12%
Unknown 23 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 18. 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 01 February 2024.
All research outputs
#2,043,429
of 25,306,238 outputs
Outputs from Genome Medicine
#455
of 1,568 outputs
Outputs of similar age
#34,823
of 349,282 outputs
Outputs of similar age from Genome Medicine
#9
of 25 outputs
Altmetric has tracked 25,306,238 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,568 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.0. This one has gotten more attention than average, scoring higher than 71% 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 349,282 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 90% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.