通过模式识别辅助的呼出气分析，使用超灵敏化学电阻式一氧化氮（NO）气体传感器对哮喘进行临床诊断并监测其严重程度。
Clinically Diagnose Asthma and Monitor Its Severity Using an Ultrasensitive Chemiresistive Nitric Oxide (NO) Gas Sensor via Exhaled Breath Analysis Assisted by Pattern Recognition.
作者信息
Yin Peisi, You Xiaoyu, Cui Xinyue, Tang Zhipeng, Yu Shanshan, Fu Huaian, Song Fei, Zhang Kai, Zhao Xin, Wang Lipeng, Tian Huanhuan, Feng Xiaoyu, Li Ping, Liu Jinping, Zhai Nailiang, Jing Qiang, Han Shasha, Liu Bo
机构信息
School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China.
Laboratory of Functional Molecules and Materials, School of Physics and Optoelectronic Engineering, Shandong University of Technology, Zibo 255000, China.
出版信息
ACS Sens. 2025 Jun 27;10(6):4491-4505. doi: 10.1021/acssensors.5c00772. Epub 2025 Jun 5.
Fractional exhaled nitric oxide (FeNO) is widely recognized as a reliable biomarker for asthma. FeNO sensors can help diagnose asthma and monitor its severity. In this study, an ultrasensitive chemiresistive gas sensor, sensitive to the key breath biomarkers of asthma─nitric oxide (NO) and HS─was fabricated using Ag-decorated ZnO. The sensor exhibits detection limits of 5 ppb for NO and 50 ppb for HS, and it can discriminate 10 ppb NO and 60 ppb HS from the exhaled breaths. Clinically, a total of 80 exhaled breath samples were collected and tested, including 40 from asthma patients (APs) and 40 from healthy control subjects (HCs). The AP group was effectively distinguished from the HC group using a pattern recognition algorithm (PCA), attributed to the sensor's beneficial cross-sensitivity to asthma biomarkers. A diagnostic model distinguishing asthma from non-asthma was constructed using the support vector machine (SVM) algorithm, achieving an overall accuracy, sensitivity, and specificity of 0.81, 0.88, and 0.75, respectively. The area under the curve (AUC) value for all subjects in the receiver operating characteristic (ROC) curve was 0.92. The severity of asthma in three inpatients was monitored using the clinical evaluation method of diurnal peak expiratory flow (PEF) variation, alongside our sensor. The sensor's response values exhibited a strong correlation ( = -0.74 ( < 0.05)) with the diurnal PEF variation values. To validate the sensor's diagnostic capability, six breath samples from both HCs and APs were tested simultaneously using our sensor and a commercial electrochemical NO sensor utilized clinically. With = -0.98 ( < 0.05) and = 0.94, a strong linear relationship between two types of response values was observed, confirming the sensor's accuracy and reliability in detecting NO concentrations in exhaled breath. Theoretical adsorption models of NO on the surface of the sensor were constructed using DFT calculations to elucidate the mechanisms driving the sensor's ultrasensitivity. Overall, the sensor demonstrates a significant potential for use in clinical practice to diagnose asthma and monitor its severity.
呼出一氧化氮分数（FeNO）被广泛认为是哮喘的可靠生物标志物。FeNO传感器有助于诊断哮喘并监测其严重程度。在本研究中，使用Ag修饰的ZnO制备了一种对哮喘关键呼吸生物标志物一氧化氮（NO）和硫化氢（HS）敏感的超灵敏化学电阻式气体传感器。该传感器对NO的检测限为5 ppb，对HS的检测限为50 ppb，并且能够从呼出气体中区分出10 ppb的NO和60 ppb的HS。临床上，共收集并测试了80份呼出气体样本，其中40份来自哮喘患者（APs），40份来自健康对照受试者（HCs）。使用模式识别算法（PCA）有效地将AP组与HC组区分开来，这归因于该传感器对哮喘生物标志物具有有益的交叉敏感性。使用支持向量机（SVM）算法构建了区分哮喘与非哮喘的诊断模型，总体准确率、敏感性和特异性分别达到0.81、0.88和0.75。受试者工作特征（ROC）曲线中所有受试者的曲线下面积（AUC）值为0.92。使用日间呼气峰值流速（PEF）变化的临床评估方法以及我们的传感器监测了三名住院患者的哮喘严重程度。该传感器的响应值与日间PEF变化值呈现出很强的相关性（r = -0.74，P < 0.05）。为了验证该传感器的诊断能力，同时使用我们的传感器和临床上使用的商业电化学NO传感器对来自HCs和APs的六个呼吸样本进行了测试。两者的相关系数r = -0.98（P < 0.05），且一致性指数ICC = 0.94，观察到两种响应值之间存在很强的线性关系，证实了该传感器在检测呼出气体中NO浓度方面的准确性和可靠性。使用密度泛函理论（DFT）计算构建了NO在传感器表面的理论吸附模型，以阐明驱动该传感器超灵敏性的机制。总体而言，该传感器在临床实践中用于诊断哮喘和监测其严重程度具有巨大潜力。
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