Reference : Exhaled human breath analysis in active pulmonary tuberculosis diagnostics by compreh...
Scientific journals : Article
Human health sciences : Cardiovascular & respiratory systems
http://hdl.handle.net/2268/229651
Exhaled human breath analysis in active pulmonary tuberculosis diagnostics by comprehensive gas chromatography-mass spectrometry and chemometric techniques
English
Beccaria, M. [Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, United States of America. KU Leuven-University of Leuven, Department for Pharmaceutical and Pharmacological Sciences, Leuven, B-3000, Belgium]
Bobak, C. [> >]
Maitshotlo, B. [> >]
Mellors, T. R. [> >]
Purcaro, Giorgia mailto [Université de Liège - ULiège > Agronomie, Bio-ingénierie et Chimie (AgroBioChem) > Chimie des agro-biosystèmes >]
Franchina, Flavio mailto [Université de Liège - ULiège > Département de chimie (sciences) > Chimie analytique, organique et biologique >]
Rees, C. A. [> >]
Nasir, M. [> >]
Black, A. [> >]
Hill, J. E. [> >]
2018
Journal of Breath Research
NLM (Medline)
13
1
016005
Yes (verified by ORBi)
International
17527163
[en] Tuberculosis (TB) is the deadliest infectious disease, and yet accurate diagnostics for the disease are unavailable for many subpopulations. In this study, we investigate the possibility of using human breath for the diagnosis of active TB among TB suspect patients, considering also several risk factors for TB for smokers and those with human immunodeficiency virus (HIV). The analysis of exhaled breath, as an alternative to sputum-dependent tests, has the potential to provide a simple, fast, non-invasive, and readily available diagnostic service that could positively change TB detection. A total of 50 individuals from a clinic in South Africa were included in this pilot study. Human breath has been investigated in the setting of active TB using the thermal desorption-comprehensive two-dimensional gas chromatography-time of flight mass spectrometry methodology and chemometric techniques. From the entire spectrum of volatile metabolites in breath, three machine learning algorithms (support vector machines, partial least squares discriminant analysis, and random forest) to select discriminatory volatile molecules that could potentially be useful for active TB diagnosis were employed. Random forest showed the best overall performance, with sensitivities of 0.82 and 1.00 and specificities of 0.92 and 0.60 in the training and test data respectively. Unsupervised analysis of the compounds implicated by these algorithms suggests that they provide important information to cluster active TB from other patients. These results suggest that developing a non-invasive diagnostic for active TB using patient breath is a potentially rich avenue of research, including among patients with HIV comorbidities.
Researchers ; Professionals
http://hdl.handle.net/2268/229651
10.1088/1752-7163/aae80e

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