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Noninvasive Diagnosis of Gastric Cancer Based on Breath Analysis with a Tubular Surface-Enhanced Raman Scattering Sensor
ACS Sensors ( IF 8.2 ) Pub Date : 2022-05-13 , DOI: 10.1021/acssensors.2c00146
Liping Huang 1, 2 , Yajie Zhu 1 , Changshun Xu 1, 2 , Yu Cai 1 , Yongdong Yi 3 , Kang Li 1 , Xueqian Ren 1, 2 , Danfeng Jiang 1, 2 , Yuancai Ge 1, 2 , Xiaohu Liu 1, 2 , Weijian Sun 3 , Qingwen Zhang 1, 2 , Yi Wang 1, 2
Affiliation  

SERS-based breath analysis as an emerging technique has attracted increasing attention in cancer screening. Here, eight aldehydes and ketones in the human breath are reported as the VOC biomarkers identified by gas chromatography–mass spectrometry (GC-MS) and applied further for the noninvasive diagnosis of gastric cancer (GC) with a tubular SERS sensor. The tubular SERS sensor is prepared with a glass capillary loaded with ZIF-67-coated silver particles (Ag@ZIF-67), which offers Raman enhancement from the plasmonic nanoparticles and gas enrichment from the metal–organic framework (MOF) shells. The composite materials are modified with 4-aminothiophenol (4-ATP) to capture different aldehyde and ketone compounds. The tubular sensor is served simultaneously as a gas flow channel and a detection chamber, bringing a higher gas capture efficiency than the planar SERS sensor. As a proof-of-concept, the tubular SERS sensor is successfully employed to screen gastric cancer patients with an accuracy of 89.83%, based on the noninvasive, rapid, and easily operated breath analysis. The results demonstrate that the established breath analysis method provides an excellent alternative for the screening of GC and other diseases.

中文翻译:

基于管状表面增强拉曼散射传感器呼吸分析的胃癌无创诊断

基于 SERS 的呼吸分析作为一种新兴技术在癌症筛查中引起了越来越多的关注。在这里,人类呼吸中的八种醛和酮被报告为通过气相色谱-质谱 (GC-MS) 鉴定的 VOC 生物标志物,并进一步用于使用管状 SERS 传感器对胃癌 (GC) 进行无创诊断。管状 SERS 传感器由装有 ZIF-67 涂层银颗粒 (Ag@ZIF-67) 的玻璃毛细管制备,它提供等离子体纳米粒子的拉曼增强和金属有机框架 (MOF) 壳的气体富集。复合材料用 4-氨基苯硫酚 (4-ATP) 改性以捕获不同的醛和酮化合物。管状传感器同时用作气体流动通道和检测室,带来比平面 SERS 传感器更高的气体捕获效率。作为概念验证,基于无创、快速且易于操作的呼吸分析,管状 SERS 传感器成功用于筛查胃癌患者,准确率达 89.83%。结果表明,所建立的呼吸分析方法为筛查 GC 和其他疾病提供了一种极好的替代方法。
更新日期:2022-05-13
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