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Gene expression profiling identifies candidate biomarkers for active and latent tuberculosis

Overview of attention for article published in BMC Bioinformatics, January 2016
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  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

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2 patents

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Title
Gene expression profiling identifies candidate biomarkers for active and latent tuberculosis
Published in
BMC Bioinformatics, January 2016
DOI 10.1186/s12859-015-0848-x
Pubmed ID
Authors

Shih-Wei Lee, Lawrence Shih-Hsin Wu, Guan-Mau Huang, Kai-Yao Huang, Tzong-Yi Lee, Julia Tzu-Ya Weng

Abstract

Tuberculosis (TB) is a serious infectious disease in that 90 % of those latently infected with Mycobacterium tuberculosis present no symptoms, but possess a 10 % lifetime chance of developing active TB. To prevent the spread of the disease, early diagnosis is crucial. However, current methods of detection require improvement in sensitivity, efficiency or specificity. In the present study, we conducted a microarray experiment, comparing the gene expression profiles in the peripheral blood mononuclear cells among individuals with active TB, latent infection, and healthy conditions in a Taiwanese population. Bioinformatics analysis revealed that most of the differentially expressed genes belonged to immune responses, inflammation pathways, and cell cycle control. Subsequent RT-PCR validation identified four differentially expressed genes, NEMF, ASUN, DHX29, and PTPRC, as potential biomarkers for the detection of active and latent TB infections. Receiver operating characteristic analysis showed that the expression level of PTPRC may discriminate active TB patients from healthy individuals, while ASUN could differentiate between the latent state of TB infection and healthy condidtion. In contrast, DHX29 may be used to identify latently infected individuals among active TB patients or healthy individuals. To test the concept of using these biomarkers as diagnostic support, we constructed classification models using these candidate biomarkers and found the Naïve Bayes-based model built with ASUN, DHX29, and PTPRC to yield the best performance. Our study demonstrated that gene expression profiles in the blood can be used to identify not only active TB patients, but also to differentiate latently infected patients from their healthy counterparts. Validation of the constructed computational model in a larger sample size would confirm the reliability of the biomarkers and facilitate the development of a cost-effective and sensitive molecular diagnostic platform for TB.

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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 %
Germany 1 <1%
South Africa 1 <1%
Unknown 131 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 18%
Researcher 16 12%
Student > Master 15 11%
Student > Bachelor 15 11%
Student > Doctoral Student 11 8%
Other 29 22%
Unknown 23 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 33 25%
Immunology and Microbiology 23 17%
Agricultural and Biological Sciences 13 10%
Medicine and Dentistry 12 9%
Unspecified 4 3%
Other 16 12%
Unknown 32 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 17 November 2022.
All research outputs
#6,947,571
of 23,257,423 outputs
Outputs from BMC Bioinformatics
#2,631
of 7,362 outputs
Outputs of similar age
#111,102
of 397,104 outputs
Outputs of similar age from BMC Bioinformatics
#54
of 143 outputs
Altmetric has tracked 23,257,423 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 7,362 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 63% 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 397,104 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 143 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 60% of its contemporaries.