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Metabolomics: Applications and Promise in Mycobacterial Disease

Overview of attention for article published in Annals of the American Thoracic Society, September 2015
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  • Above-average Attention Score compared to outputs of the same age (63rd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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1 X user
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1 patent

Readers on

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109 Mendeley
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2 CiteULike
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Article details
Title
Metabolomics: Applications and Promise in Mycobacterial Disease
Published in
Annals of the American Thoracic Society, September 2015
DOI 10.1513/annalsats.201505-279ps
Pubmed ID
Authors
Abstract

Until recently, the study of mycobacterial diseases was trapped in culture-based technology that is more than a century old. The use of nucleic acid amplification is changing this, and powerful new technologies are on the horizon. Metabolomics, which is the study of sets of metabolites of both the bacteria and host, is being used to clarifying mechanisms of disease, and can identify changes leading to better diagnosis, treatment, and prognostication of mycobacterial diseases. Metabolomic profiles are arrays of biochemical products of genes in their environment. These complex patterns are biomarkers that can allow a more complete understanding of cell function, dysfunction, and perturbation than genomics or proteomics. Metabolomics could herald sweeping advances in personalized medicine and clinical trial design, but the challenges in metabolomics are also great. Measured metabolite concentrations vary with the timing within a condition, the intrinsic biology, the instruments, and the sample preparation. Metabolism profoundly changes with age, gender, variations in gut microbial flora, and lifestyle. Validation of biomarkers is complicated by measurement accuracy, selectivity, linearity, reproducibility, robustness, and limits of detection. The statistical challenges include analysis, interpretation, and description of the vast amount data generated. Despite these drawbacks, metabolomics provides a great opportunity and potential for understanding and managing mycobacterial diseases.  .

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X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 109 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
South Africa 1 <1%
Unknown 108 99%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 18 17%
Student > Ph. D. Student 16 15%
Student > Doctoral Student 10 9%
Student > Master 10 9%
Student > Postgraduate 6 6%
Other 19 17%
Unknown 30 28%
Readers by discipline
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 20 18%
Medicine and Dentistry 15 14%
Agricultural and Biological Sciences 12 11%
Immunology and Microbiology 6 6%
Chemistry 5 5%
Other 15 14%
Unknown 36 33%
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 03 March 2026.
All research outputs
#10,975,863
of 32,261,701 outputs
Outputs from Annals of the American Thoracic Society
#2,017
of 3,650 outputs
Outputs of similar age
#101,833
of 292,548 outputs
Outputs of similar age from Annals of the American Thoracic Society
#30
of 61 outputs
Altmetric has tracked 32,261,701 research outputs across all sources so far. This one has received more attention than most of these and is in the 65th percentile.
So far Altmetric has tracked 3,650 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 16.1. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
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 292,548 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 63% of its contemporaries.
We're also able to compare this research output to 61 others from the same source and published within six weeks on either side of this one. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.