Na Jukwan, Hwang Euimin, Choi Jun Shik, Ji Min-Jin, Noh Young, Lim Yong-Beom, Choi Heon-Jin
Department of Materials Science and Engineering, Yonsei University, Seoul 03722, Republic of Korea.
Department of Health Science and Technology, GAIHST, Gachon University, Incheon 21999, Republic of Korea.
ACS Omega. 2020 Oct 13;5(42):27295-27303. doi: 10.1021/acsomega.0c03578. eCollection 2020 Oct 27.
Detecting amyloid beta (Aβ) in unpurified blood to diagnose Alzheimer's disease (AD) is challenging owing to low concentrations of Aβ and the presence of many other substances in the blood. Here, we propose a 3D sensor for AD diagnosis using blood plasma, with pairs of 3D silicon micropillar electrodes with a comprehensive circuit configuration. The sensor is developed with synthesized artificial peptide and impedance analysis based on a maximum signal-to-noise ratio. Its sensitivity and selectivity were verified using an test based on samples of human blood serum, which showed its feasibility for application in diagnosis of AD by testing blood plasma of the AD patient. The 3D sensor is designed to improve reliability by checking the impedance of each pair multiple times via constructing a reference pair and a working pair on the same sensor. Therefore, we demonstrate the ability of the 3D sensor to recognize cases of AD using blood plasma and introduce its potential as a self-health care sensor for AD patients.
由于血液中β淀粉样蛋白(Aβ)浓度较低且存在许多其他物质,在未纯化的血液中检测Aβ以诊断阿尔茨海默病(AD)具有挑战性。在此,我们提出一种用于AD诊断的3D传感器,该传感器使用血浆,具有一对具有综合电路配置的3D硅微柱电极。该传感器基于最大信噪比,通过合成人工肽和阻抗分析开发而成。使用基于人血清样本的测试验证了其灵敏度和选择性,通过测试AD患者的血浆,证明了其在AD诊断中的应用可行性。通过在同一传感器上构建参考对和工作对,多次检查每对电极的阻抗,设计3D传感器以提高可靠性。因此,我们展示了3D传感器利用血浆识别AD病例的能力,并介绍了其作为AD患者自我保健传感器的潜力。