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Detecting Pulmonary Oxygen Toxicity Using eNose Technology and Associations between Electronic Nose and Gas Chromatography–Mass Spectrometry Data

机译:使用eNose技术检测肺氧中毒以及电子鼻和气相色谱-质谱数据之间的关联

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摘要

Exposure to oxygen under increased atmospheric pressures can induce pulmonary oxygen toxicity (POT). Exhaled breath analysis using gas chromatography–mass spectrometry (GC–MS) has revealed that volatile organic compounds (VOCs) are associated with inflammation and lipoperoxidation after hyperbaric–hyperoxic exposure. Electronic nose (eNose) technology would be more suited for the detection of POT, since it is less time and resource consuming. However, it is unknown whether eNose technology can detect POT and whether eNose sensor data can be associated with VOCs of interest. In this randomized cross-over trial, the exhaled breath from divers who had made two dives of 1 h to 192.5 kPa (a depth of 9 m) with either 100% oxygen or compressed air was analyzed, at several time points, using GC–MS and eNose. We used a partial least square discriminant analysis, eNose discriminated oxygen and air dives at 30 min post dive with an area under the receiver operating characteristics curve of 79.9% (95%CI: 61.1–98.6; = 0.003). A two-way orthogonal partial least square regression (O2PLS) model analysis revealed an R² of 0.50 between targeted VOCs obtained by GC–MS and eNose sensor data. The contribution of each sensor to the detection of targeted VOCs was also assessed using O2PLS. When all GC–MS fragments were included in the O2PLS model, this resulted in an R² of 0.08. Thus, eNose could detect POT 30 min post dive, and the correlation between targeted VOCs and eNose data could be assessed using O2PLS.
机译:在升高的大气压下暴露于氧气中会导致肺部氧气中毒(POT)。使用气相色谱-质谱(GC-MS)进行呼气分析表明,高压-高氧暴露后,挥发性有机化合物(VOC)与炎症和脂过氧化有关。电子鼻(eNose)技术将更适合于POT的检测,因为它的时间和资源消耗更少。但是,尚不清楚eNose技术是否可以检测到POT,以及eNose传感器数据是否可以与感兴趣的VOC相关联。在这项随机交叉试验中,使用GC –在多个时间点分析了两次潜水的呼气,这些潜水员分别用100%氧气或压缩空气进行了1 h至192.5 kPa(9 m深度)两次潜水MS和eNose。我们使用了偏最小二乘判别分析,在潜水后30分钟,eNose判别了氧气和空气潜水,接收器工作特性曲线下的面积为79.9%(95%CI:61.1–98.6; = 0.003)。双向正交偏最小二乘(O2PLS)模型分析表明,通过GC-MS获得的目标VOC与eNose传感器数据之间的R²为0.50。还使用O2PLS评估了每个传感器对目标VOC检测的贡献。当所有GC-MS片段都包括在O2PLS模型中时,R²为0.08。因此,eNose可以在潜水后30分钟检测到POT,并且可以使用O2PLS评估目标VOC与eNose数据之间的相关性。

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