Song Chao, Que Suya, Heimer Lucas, Que Long
Electrical and Computer Engineering Department, Iowa State University, Ames, IA 50011, USA.
Ames High School, Ames, IA 50010, USA.
Micromachines (Basel). 2020 Jun 28;11(7):629. doi: 10.3390/mi11070629.
Alzheimer's disease (AD), Parkinson's disease (PD) and glaucoma are all regarded as neurodegenerative diseases (neuro-DDs) because these diseases are highly related to the degeneration loss of functions and death of neurons with aging. The conventional diagnostic methods such as neuroimaging for these diseases are not only expensive but also time-consuming, resulting in significant financial burdens for patients and public health challenge for nations around the world. Hence early detection of neuro-DDs in a cost-effective and rapid manner is critically needed. For the past decades, some chip-based detection technologies have been developed to address this challenge, showing great potential in achieving point-of-care (POC) diagnostics of neuro-DDs. In this review, chip-based detection of neuro-DDs' biomarkers enabled by different transducing mechanisms is evaluated.
阿尔茨海默病（AD）、帕金森病（PD）和青光眼都被视为神经退行性疾病（神经退行性疾病），因为这些疾病与随着年龄增长神经元功能的退化丧失和死亡高度相关。针对这些疾病的传统诊断方法，如神经成像，不仅昂贵而且耗时，给患者带来了巨大的经济负担，也给世界各国的公共卫生带来了挑战。因此，迫切需要以具有成本效益且快速的方式早期检测神经退行性疾病。在过去几十年中，已经开发了一些基于芯片的检测技术来应对这一挑战，在实现神经退行性疾病的即时检测（POC）诊断方面显示出巨大潜力。在这篇综述中，评估了由不同转导机制实现的基于芯片的神经退行性疾病生物标志物检测。
