Peng Liying, Jiang Dandan, Wang Zhenxin, Liu Jiwei, Li Haiyang
Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, People's Republic of China.
University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China.
Sci Rep. 2016 Mar 15;6:23095. doi: 10.1038/srep23095.
Exhaled nitric oxide (NO) is one of the most promising breath markers for respiratory diseases. Its profile for exhalation and the respiratory NO production sites can provide useful information for medical disease diagnosis and therapeutic procedures. However, the high-level moisture in exhaled gas always leads to the poor selectivity and sensitivity for ion spectrometric techniques. Herein, a method based on fast non-equilibrium dilution ion mobility spectrometry (NED-IMS) was firstly proposed to directly monitor the exhaled NO profile on line. The moisture interference was eliminated by turbulently diluting the original moisture to 21% of the original with the drift gas and dilution gas. Weak enhancement was observed for humid NO response and its limit of detection at 100% relative humidity was down to 0.58 ppb. The NO concentrations at multiple exhalation flow rates were measured, while its respiratory production sites were determined by using two-compartment model (2CM) and Högman and Meriläinen algorithm (HMA). Last but not the least, the NO production sites were analyzed hourly to tentatively investigate the daily physiological process of NO. The results demonstrated the capacity of NED-IMS in the real-time analysis of exhaled NO and its production sites for clinical diagnosis and assessment.
呼出一氧化氮（NO）是呼吸系统疾病最有前景的呼吸标志物之一。其呼气特征和呼吸道NO产生部位可为医学疾病诊断和治疗程序提供有用信息。然而，呼出气体中的高湿度总是导致离子光谱技术的选择性和灵敏度较差。在此，首次提出了一种基于快速非平衡稀释离子迁移谱（NED-IMS）的方法来在线直接监测呼出NO特征。通过用漂移气和稀释气将原始水分湍流稀释至原始水分的21%，消除了水分干扰。观察到潮湿NO响应有微弱增强，其在100%相对湿度下的检测限降至0.58 ppb。测量了多个呼气流量下的NO浓度，同时使用双室模型（2CM）和Högman与Meriläinen算法（HMA）确定其呼吸道产生部位。最后但同样重要的是，每小时分析NO产生部位，以初步研究NO的日常生理过程。结果证明了NED-IMS在呼出NO及其产生部位实时分析用于临床诊断和评估方面的能力。
