Preliminary investigation of human exhaled breath for tuberculosis diagnosis by multidimensional gas chromatography - Time of flight mass spectrometry and machine learning

Autor: Marc-Antoine Jean-Juste, Theodore R Mellors, Marco Beccaria, Hannah K. Systrom, Jean W. Pape, Patrice Severe, Jean W. Sairistil, Vanessa Rivera, Christiaan A. Rees, Jacky S. Petion, Mavra Nasir, Jane E. Hill, Kerline Lavoile, Peter F. Wright
Rok vydání: 2017
Předmět:
Adult
Male
Tuberculosis
Adolescent
Clinical Biochemistry
Machine learning
computer.software_genre
01 natural sciences
Biochemistry
Sputum sample
Gas Chromatography-Mass Spectrometry
NO
Comprehensive two-dimensional gas
Analytical Chemistry
Machine Learning
03 medical and health sciences
Young Adult
0302 clinical medicine
Tuberculosis diagnosis
Active tb
medicine
Humans
PE4_5
030212 general & internal medicine
Volatile metabolites
Volatile Organic Compounds
Chemistry
business.industry
Pulmonary tuberculosis
010401 analytical chemistry
Breath analysis
LS6_11
Cell Biology
General Medicine
Breath analysis
Comprehensive two-dimensional gas
chromatography
Pulmonary tuberculosis
Machine learning
Volatile organic compounds
Middle Aged
Active tuberculosis
medicine.disease
0104 chemical sciences
Breath gas analysis
Breath Tests
chromatography
Female
Artificial intelligence
Tuberculosis control
business
computer
Zdroj: Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.
ISSN: 1873-376X
Popis: Tuberculosis (TB) remains a global public health malady that claims almost 1.8 million lives annually. Diagnosis of TB represents perhaps one of the most challenging aspects of tuberculosis control. Gold standards for diagnosis of active TB (culture and nucleic acid amplification) are sputum-dependent, however, in up to a third of TB cases, an adequate biological sputum sample is not readily available. The analysis of exhaled breath, as an alternative to sputum-dependent tests, has the potential to provide a simple, fast, and non-invasive, and ready-available diagnostic service that could positively change TB detection. Human breath has been evaluated in the setting of active tuberculosis using thermal desorption-comprehensive two-dimensional gas chromatography–time of flight mass spectrometry methodology. From the entire spectrum of volatile metabolites in breath, three random forest machine learning models were applied leading to the generation of a panel of 46 breath features. The twenty-two common features within each random forest model used were selected as a set that could distinguish subjects with confirmed pulmonary M. tuberculosis infection and people with other pathologies than TB.
Databáze: OpenAIRE
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