肺结核
痰
化学
气相色谱法
结核分枝杆菌
气体分析呼吸
质谱法
呼出的空气
色谱法
呼出气冷凝液
气相色谱-质谱法
呼气
医学
内科学
病理
放射科
毒理
哮喘
生物
作者
Marco Beccaria,Theodore Mellors,Jacky S. Petion,Christiaan A. Rees,Mavra Nasir,Hannah Systrom,Jean W. Sairistil,Marc-Antoine Jean-Juste,Vanessa Rivera,Kerline Lavoile,Patrice Sévère,Jean William Pape,Peter F. Wright,Jane E. Hill
标识
DOI:10.1016/j.jchromb.2018.01.004
摘要
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.
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