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ORFeome-based identification of biomarkers for serodiagnosis of Mycobacterium tuberculosis latent infection

Overview of attention for article published in BMC Infectious Diseases, December 2017
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Title
ORFeome-based identification of biomarkers for serodiagnosis of Mycobacterium tuberculosis latent infection
Published in
BMC Infectious Diseases, December 2017
DOI 10.1186/s12879-017-2910-y
Pubmed ID
Authors

Fangbin Zhou, Xindong Xu, Sijia Wu, Xiaobing Cui, Weiqing Pan

Abstract

The challenges posed by Mycobacterium tuberculosis infection require the gradual removal of the pool of latent tuberculosis infection (LTBI). The current cell-immune-based diagnostic tests used to identify LTBI individuals have several irreversible drawbacks. In the present study, we attempted to identify novel diagnostic antigens for LTBI. A high-throughput glutathione S-transferase (GST)-fusion technology was used to express over 409 TB proteins and sera from LTBI and healthy individuals was used to interrogate these GST-TB fusion proteins. Of 409 TB proteins, sixty-three reacted seropositive and defined the immuno-ORFeome of latent M. tuberculosis. Within the immuno-ORFeome, the rare targets were predominantly latency-associated proteins and secreted proteins, while the preferentially recognized antigens tended to be transmembrane proteins. Six of novel highly-reactive antigens had the potential to distinguish LTBI from active TB and healthy individuals. A multiple-antigen combination set was selected through analysis of various combinations. A panel of 94 archived serum samples was used to validate the diagnostic performance of the multiple-antigen combination set, which had sensitivity of 66.1% (95% CI 52.9, 77.4) and specificity of 87.5% (95% CI 70.1, 95.1). These results provide experimental evidence of the immunogenicity of novel TB proteins that are suitable for the development of serodiagnostic tools for LTBI.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 16%
Researcher 4 16%
Student > Bachelor 3 12%
Student > Doctoral Student 2 8%
Student > Postgraduate 2 8%
Other 6 24%
Unknown 4 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 28%
Agricultural and Biological Sciences 4 16%
Medicine and Dentistry 4 16%
Engineering 2 8%
Social Sciences 1 4%
Other 3 12%
Unknown 4 16%
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 02 January 2018.
All research outputs
#17,925,346
of 23,015,156 outputs
Outputs from BMC Infectious Diseases
#5,165
of 7,723 outputs
Outputs of similar age
#309,839
of 441,975 outputs
Outputs of similar age from BMC Infectious Diseases
#102
of 160 outputs
Altmetric has tracked 23,015,156 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 7,723 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one is in the 26th percentile – i.e., 26% of its peers scored the same or lower than it.
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We're also able to compare this research output to 160 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.