Exhaled human breath analysis in active pulmonary tuberculosis diagnostics by comprehensive gas chromatography-mass spectrometry and chemometric techniques
| Autor: | Marco, Beccaria, Carly, Bobak, Boitumelo, Maitshotlo, Theodore R, Mellors, Giorgia, Purcaro, Flavio A, Franchina, Christiaan A, Rees, Mavra, Nasir, Wendy S, Stevens, Lesley E, Scott, Andrew, Black, Jane E, Hill |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Pulmonary and Respiratory Medicine Adult Male medicine.medical_specialty Tuberculosis Support Vector Machine Human immunodeficiency virus (HIV) Pilot Projects medicine.disease_cause 01 natural sciences Sensitivity and Specificity Article Gas Chromatography-Mass Spectrometry NO Machine Learning 03 medical and health sciences Tuberculosis diagnosis Pulmonary tuberculosis Active tb medicine VOCs metabolomics pulmonary tuberculosis comprehensive two-dimensional gas chromatography machine learning human exhaled breath Humans Least-Squares Analysis Volatile metabolites Intensive care medicine Tuberculosis Pulmonary Principal Component Analysis comprehensive two-dimensional gas chromatography business.industry 010401 analytical chemistry VOCs Discriminant Analysis human exhaled breath medicine.disease metabolomics 0104 chemical sciences 030104 developmental biology Breath gas analysis Breath Tests ROC Curve Exhalation Female Gas chromatography–mass spectrometry business pulmonary tuberculosis |
| Zdroj: | Journal of breath research. 13(1) |
| ISSN: | 1752-7163 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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