Noninvasive Strategy Based on Real-Time in Vivo Cataluminescence Monitoring for Clinical Breath Analysis
Noninvasive Strategy Based on Real-Time in Vivo Cataluminescence Monitoring for Clinical Breath Analysis
复制标题
基于实时体内辉光监测的无创策略用于临床呼吸分析
DOI：
10.1021/acs.analchem.6b03898
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发表时间：
2017
影响因子：
7.4
通讯作者：
Hu Yufei
中科院分区：
文献类型：
--
作者：
Zhang Runkun;Huang Wanting;Li Gongke;Hu Yufei
发展体内实时分析的无创方法具有重要意义，为医学研究和临床诊断提供了有力的工具。在目前的工作中，我们描述了一种基于催化发光（CTL）的新策略，用于实时体内临床呼吸分析。为了说明这一策略，设计了一种自制的实时CTL监测系统，其特征是将在线采样装置与七氟醚（SVF）CTL传感器耦合，并提出了一种实时体内监测呼出气中SVF的方法。通过分析真实呼出气样本来评估该方法的准确性，并将结果与GC/MS获得的结果进行比较。两种方法得到的测量数据吻合较好。随后，将该方法应用于对照组大鼠模型呼出气中SVF的实时监测，以研究消除药代动力学。为了进一步探讨该方法的临床应用潜力，采用该方法监测了对照组、肝纤维化组、酒精肝组、非酒精性脂肪肝组大鼠模型中SVF的消除药代动力学。将不同组的药代动力学原始数据标准化并随后进行线性判别分析（LDA）。这些数据被转换为规范评分，可视化良好聚类，分类准确度为 100%，留一交叉验证程序的总体准确度为 88%，从而表明该方法在肝病诊断方面的潜力。我们的策略无疑为无痛、无创的实时临床分析打开了一扇新的大门，也为CTL指明了一个有前景的发展方向。
The development of noninvasive methods for real-time in vivo analysis is of great significant, which provides powerful tools for medical research and clinical diagnosis. In the present work, we described a new strategy based on cataluminescence (CTL) for real-time in vivo clinical breath analysis. To illustrate such strategy, a homemade real-time CTL monitoring system characterized by coupling an online sampling device with a CTL sensor for sevoflurane (SVF) was designed, and a real-time in vivo method for the monitoring of SVF in exhaled breath was proposed. The accuracy of the method was evaluated by analyzing the real exhaled breath samples, and the results were compared with those obtained by GC/MS. The measured data obtained by the two methods were in good agreement. Subsequently, the method was applied to real-time monitoring of SVF in exhaled breath from rat models of the control group to investigate elimination pharmacokinetics. In order to further probe the potential of the method for clinical application, the elimination pharmacokinetics of SVF from rat models of control group, liver fibrosis group alcohol liver group, and nonalcoholic fatty liver group were monitored by the method. The raw data of pharmacokinetics of different groups were normalized and subsequently subjected to linear discriminant analysis (LDA). These data were transformed to canonical scores which were visualized as well-clustered with the classification accuracy of 100%, and the overall accuracy of leave-one-out cross-validation procedure is 88%, thereby indicating the utility of the potential of the method for liver disease diagnosis. Our strategy undoubtedly opens up a new door for real-time clinical analysis in a pain-free and noninvasive way and also guides a promising development direction for CTL.