Preliminary investigation of human exhaled breath for tuberculosis diagnosis by multidimensional gas chromatography - Time of flight mass spectrometry and machine learning
Preliminary investigation of human exhaled breath for tuberculosis diagnosis by multidimensional gas chromatography - Time of flight mass spectrometry and machine learning
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DOI：
10.1016/j.jchromb.2018.01.004
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发表时间：
2018-02-01
影响因子：
3
通讯作者：
Hill, Jane E.
中科院分区：
文献类型：
--
作者：
Beccaria, Marco;Mellors, Theodore R.;Hill, Jane E.
结核病仍然是一种全球性的公共卫生疾病，每年夺去近180万人的生命。结核病的诊断可能是结核病控制最具挑战性的方面之一。诊断活动性结核病的金标准（培养和核酸扩增）依赖于结核病，然而，在多达三分之一的结核病病例中，无法随时获得足够的生物痰样本。呼出气分析作为肺结核依赖性检测的替代方法，有可能提供一种简单、快速、非侵入性和现成的诊断服务，从而积极改变结核病检测。采用热脱附-全二维气相色谱-飞行时间质谱联用技术对活动性肺结核患者的呼吸进行了研究。根据呼吸中挥发性代谢物的整个光谱，应用三个随机森林机器学习模型，从而生成一组46个呼吸特征。选择每个随机森林模型中的22个共同特征作为一组，可以区分确认患有肺M的受试者。结核病感染和患有结核病以外的其他疾病的人。
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.