用于识别非酒精性脂肪肝生物标志物的小型呼气酒精测试仪

D.Z. Wang, X.Y. Hua, G.Q. Hu, Z.H. Wang, F.F. Yan, K.N. Zhang, C. Cheng, S.B. Li, X.Y. Wu, H.R. Wang
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摘要

非酒精性脂肪肝(NAFLD)是一种全球流行的慢性肝病。目前,该病的诊断主要依靠影像学和组织学检查,这些检查都是侵入性的,而且在早期容易误诊。针对这些局限性,检测和分析人体呼气中的挥发性有机化合物(VOCs)可作为一种快速、无创的非酒精性脂肪肝筛查方法。本研究开发了一种紧凑型呼气分析仪,利用微型气相色谱芯片和 STM32 微控制器作为主控芯片,管理气流、温度并接收光离子化检测器的终端信号。在实验中,选取了五种挥发性有机化合物(戊烷、丙酮、甲苯、辛烷和癸烷)组成的混合气体作为人体呼气中的模拟典型疾病生物标志物,以考察呼气分析仪的性能,并优化多极和宽沸程呼气样本的测试条件。结果表明,呼气分析仪可检测异戊二烯和丙酮等低沸点成分(< 100°C),检测限小于 50 ppb,而这两种成分是非酒精性脂肪肝的常见生物标志物。此外,还收集了 35 名非患病者的呼气样本,并通过将异戊二烯浓度提高 10 ppb 模拟非酒精性脂肪肝早期患者样本。利用卷积神经网络(CNN)识别气相色谱中的挥发性有机化合物特征,分类模型的预测准确率达到 85%。因此,紧凑型呼气式酒精检测仪有望应用于非酒精性脂肪肝的早期快速筛查。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A compact breath breathalyzer for identifying the non-alcoholic fatty liver disease biomarker
Non-alcoholic fatty liver disease (NAFLD) is a prevalent chronic liver disease worldwide. Currently, its diagnosis relies primarily on imaging and histological examinations, which are invasive and prone to misdiagnosis in the early stage. To address these limitations, detection and analysis of volatile organic compounds (VOCs) in human breath can be a rapid and non-invasive screening method for NAFLD. In this study, a compact breath breathalyzer was developed, utilizing a miniaturized gas chromatography chip with the STM32 microcontroller as the main control chip to manage airflow, temperature, and receive terminal signals from the photoionization detector. In the experiment, a gas mixture comprising five VOCs (pentane, acetone, toluene, octane, and decane) was selected as the simulated typical disease biomarkers in human breath to investigate the breathalyzer's performance and optimize testing conditions for multi-polar and wide-boiling-range breath samples. Results show that the breathalyzer can detect low-boiling components (< 100°C) such as the isoprene and acetone, with a detection limit less than 50 ppb which are two commonly biomarkers of NAFLD. Furthermore, breath samples were collected from 35 non-diseased individuals, and NAFLD early-stage patient samples were simulated by increasing the isoprene concentration by 10 ppb. Convolutional neural network (CNN) were used to identify the VOC signatures in gas chromatograms with predictive accuracy of 85% for the classification model. Therefore, the compact breath breathalyzer has potential application in the rapid and early screening of NAFLD.
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