Zaim Omar, Diouf Alassane, El Bari Nezha, Lagdali Naoual, Benelbarhdadi Imane, Ajana Fatima Zohra, Llobet Eduard, Bouchikhi Benachir
Sensor Electronic & Instrumentation Group, Department of Physics, Faculty of Sciences, Moulay Ismaïl University of Meknes, B.P. 11201, Zitoune, Meknes, Morocco; Biosensors and Nanotechnology Group, Department of Biology, Faculty of Sciences, Moulay Ismaïl University of Meknes, B.P. 11201, Zitoune, 50003, Meknes, Morocco.
Biosensors and Nanotechnology Group, Department of Biology, Faculty of Sciences, Moulay Ismaïl University of Meknes, B.P. 11201, Zitoune, 50003, Meknes, Morocco.
Anal Chim Acta. 2021 Nov 1;1184:339028. doi: 10.1016/j.aca.2021.339028. Epub 2021 Sep 3.
Advanced stage detection of liver cirrhosis (LCi) would lead to high mortality rates in patients. Therefore, accurate and non-invasive tools for its early detection are highly needed using human emanations that may reflect this disease. Human breath, along with urine and blood, has long been one of the three main biological media for assessing human health and environmental exposure. The primary objective of this study was to explore the potential of using volatile organic compounds (VOCs) assay of exhaled breath and urine samples for the diagnosis of patients with LCi and healthy controls (HC). For this purpose, we used a hybrid electronic nose (E-nose) combining two sensor families, consisting of an array of five commercial chemical gas sensors and six interdigitated chemical gas sensors based on pristine or metal-doped WO nanowires for sensing volatile gases in exhaled breath. A voltammetric electronic tongue (VE-tongue), composed of five working electrodes, was dedicated to the analysis of urinary VOCs using cyclic voltammetry as a measurement technique. 54 patients were recruited for this study, comprising 22 patients with LCi, and 32 HC. The two-sensing systems coupled with pattern recognition methods, namely Principal Component Analysis (PCA) and Discriminant Function Analysis (DFA), were trained to classify data clusters associated with the health status of the two groups. The diagnostic performances of the E-nose and VE-tongue systems were studied by using the receiver operating characteristic (ROC) method. The use of the E-nose or the VE-tongue separately, trained with these appropriate classifiers, showed a slight overlap indicating no clear discrimination between LCi patients and HC. To improve the performance of both electronic sensing devices, an emerging strategy, namely a multi-sensor data fusion technique, was proposed as a second aim to overcome this shortcoming. The data fusion approach of the two systems, at a medium level of abstraction, has demonstrated the ability to assess human health and disease status using non-invasive screening tools based on exhaled breath and urinary VOC analysis. This suggests that exhaled breath as well as urinary VOCs are specific to a disease state and could potentially be used as diagnostic methods.
肝硬化（LCi）的晚期检测会导致患者的高死亡率。因此，迫切需要使用可能反映这种疾病的人体散发物来开发准确且无创的早期检测工具。长期以来，人体呼出的气体以及尿液和血液一直是评估人体健康和环境暴露的三种主要生物介质之一。本研究的主要目的是探索利用呼出气体和尿液样本中的挥发性有机化合物（VOCs）检测来诊断LCi患者和健康对照者（HC）的潜力。为此，我们使用了一种混合电子鼻（E-nose），它结合了两个传感器系列，其中包括一个由五个商用化学气体传感器组成的阵列，以及六个基于原始或金属掺杂的WO纳米线的叉指式化学气体传感器，用于检测呼出气体中的挥发性气体。一个由五个工作电极组成的伏安电子舌（VE-tongue），采用循环伏安法作为测量技术，专门用于分析尿液中的VOCs。本研究招募了54名患者，其中包括22名LCi患者和32名HC。将这两种传感系统与模式识别方法（即主成分分析（PCA）和判别函数分析（DFA））相结合，对与两组健康状况相关的数据聚类进行训练以进行分类。通过使用受试者工作特征（ROC）方法研究了电子鼻和电子舌系统的诊断性能。单独使用电子鼻或电子舌，并使用这些合适的分类器进行训练，结果显示有轻微重叠，表明LCi患者和HC之间没有明显的区分。为了提高这两种电子传感设备的性能，作为第二个目标，提出了一种新兴策略，即多传感器数据融合技术，以克服这一缺点。这两个系统在中等抽象水平上的数据融合方法，已证明能够使用基于呼出气体和尿液VOC分析的无创筛查工具来评估人体健康和疾病状态。这表明呼出气体以及尿液中的VOCs具有疾病特异性，有可能用作诊断方法。
