Mohamed E I, Mohamed M A, Moustafa M H, Abdel-Mageed S M, Moro A M, Baess A I, El-Kholy S M
Department of Medical Biophysics.
Department of Chemical Pathology, Medical Research Institute.
Int J Tuberc Lung Dis. 2017 Jul 1;21(7):810-817. doi: 10.5588/ijtld.16.0677.
To apply an e-nose system for monitoring headspace volatiles in biological samples from Egyptian patients with active pulmonary tuberculosis (TB) and healthy controls (HCs) and compare them with standard sputum analysis.
The study population comprised 260 (140 males, 120 females) newly diagnosed TB patients and 240 (120 males, 120 females) HCs matched by age and socio-economic level admitted to hospitals specialising in chest diseases in Alexandria, Behera, Giza and Damietta Governorates, Egypt. Participants provided a history of TB and underwent clinical examinations, chest X-ray, and microbiological and e-nose analyses. Biological samples (blood, breath, sputum and urine) were collected.
Being a confirmed TB patient was directly proportional to e-nose 10-sensor responses. Principal component analysis clusters showed a clear distinction between TB and HC groups, with variances of 93%, 85%, 75% and 95% for blood, breath, sputum and urine samples, respectively. Overall accuracy, sensitivity and specificity of the artificial neural network (ANN) analysis for classifying samples were >99%. The e-nose successfully distinguished TB patients from HC participants for all measured biological samples with great precision. With urine samples gaining broader acceptance for clinical diagnosis, an e-nose-based ANN can be a very useful tool for low-cost mass screening and early detection of TB patients in developing countries.
应用电子鼻系统监测埃及活动性肺结核患者和健康对照者生物样本中的顶空挥发物，并与标准痰液分析进行比较。
研究人群包括260名（140名男性，120名女性）新诊断的肺结核患者和240名（120名男性，120名女性）年龄和社会经济水平相匹配的健康对照者，他们均入住埃及亚历山大、贝赫拉、吉萨和达米埃塔省的胸科专科医院。参与者提供了结核病史，并接受了临床检查、胸部X光检查、微生物学和电子鼻分析。收集了生物样本（血液、呼吸、痰液和尿液）。
确诊为肺结核患者与电子鼻10传感器响应呈正比。主成分分析聚类显示肺结核组和健康对照组之间有明显区别，血液、呼吸、痰液和尿液样本的方差分别为93%、85%、75%和95%。人工神经网络（ANN）分析对样本分类的总体准确率、敏感性和特异性均>99%。电子鼻能够非常精确地从所有测量的生物样本中成功区分肺结核患者和健康对照者。随着尿液样本在临床诊断中得到更广泛的接受，基于电子鼻的人工神经网络可以成为发展中国家低成本大规模筛查和早期检测肺结核患者的非常有用的工具。
