| Title: | Preliminary investigation of human exhaled breath for tuberculosis diagnosis by multidimensional gas chromatography - Time of flight mass spectrometry and machine learning |
| Author(s): | Beccaria M; Mellors TR; Petion JS; Rees CA; Nasir M; Systrom HK; Sairistil JW; Jean-Juste MA; Rivera V; Lavoile K; Severe P; Pape JW; Wright PF; Hill JE; |
| Address: | "Thayer School of Engineering, Dartmouth College, Hanover, NH, USA. Groupe Haitien d'Etude du Sarcome de Kaposi et des Infections Opportunistes (GHESKIO), Port-au-Prince, Haiti; Department of Medicine of Weill Cornell Medical College, New York, NY, USA. Geisel School of Medicine, Dartmouth College, Hanover, NH, USA. Division of Infectious Disease and International Health, Dartmouth Hitchcock Medical Center, Lebanon, NH, USA. Thayer School of Engineering, Dartmouth College, Hanover, NH, USA; Geisel School of Medicine, Dartmouth College, Hanover, NH, USA. Electronic address: jane.e.hill@dartmouth.edu" |
| Journal Title: | J Chromatogr B Analyt Technol Biomed Life Sci |
| DOI: | 10.1016/j.jchromb.2018.01.004 |
| ISSN/ISBN: | 1873-376X (Electronic) 1570-0232 (Linking) |
| Abstract: | "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" |
| Keywords: | Adolescent Adult Breath Tests/*methods Female Gas Chromatography-Mass Spectrometry/*methods Humans *Machine Learning Male Middle Aged Tuberculosis/*diagnosis Volatile Organic Compounds/*analysis/chemistry Young Adult Breath analysis Comprehensive two-dime; |
| Notes: | "MedlineBeccaria, Marco Mellors, Theodore R Petion, Jacky S Rees, Christiaan A Nasir, Mavra Systrom, Hannah K Sairistil, Jean W Jean-Juste, Marc-Antoine Rivera, Vanessa Lavoile, Kerline Severe, Patrice Pape, Jean W Wright, Peter F Hill, Jane E eng T32 LM012204/LM/NLM NIH HHS/ Netherlands 2018/01/15 J Chromatogr B Analyt Technol Biomed Life Sci. 2018 Feb 1; 1074-1075:46-50. doi: 10.1016/j.jchromb.2018.01.004. Epub 2018 Jan 4" |
