Smartphone-based differential pulse amperometry system for real-time monitoring of levodopa with carbon nanotubes and gold nanoparticles modified screen-printing electrodes

左旋多巴 安培法 电极 计算机科学 电化学气体传感器 材料科学 纳米技术 生物医学工程 帕金森病 电化学 化学 医学 疾病 内科学 物理化学
作者
Daizong Ji,Ning Xu,Zixiang Liu,Zhouyuanjing Shi,Sze Shin Low,Jingjing Liu,Chen Cheng,Jingwen Zhu,Tingkai Zhang,Haoxuan Xu,Xiongjie Yu,Qingjun Liu
出处
期刊:Biosensors and Bioelectronics [Elsevier]
卷期号:129: 216-223 被引量:63
标识
DOI:10.1016/j.bios.2018.09.082
摘要

Parkinson's disease caused by lack of dopamine in brain is a common neurodegenerative disorder. The traditional treatment is to replenish levodopa since it could pass through blood brain barrier and form dopamine. However, its accumulation can cause patients' movement disorders and uncontrollable emotion. Therefore, it is critical to control the levodopa dosage accuracy to improve the curative effect in clinical. In this study, a smartphone-based electrochemical detection system was developed for rapid monitoring of levodopa. The system involved a disposable sensor, a hand-held electrochemical detector, and a smartphone with designed application. Single-wall carbon nanotubes and gold nanoparticles modified screen-printed electrodes were used to convert and amplify the electrochemical current signals upon presence of levodopa molecules. The electrochemical detectors were used to generate electrochemical excitation signals and detect the resultant currents. Smartphone was connected to the detector, which was used to control the detector, calculate data, and plot graph in real-time. The smartphone-based differential pulse amperometry system was demonstrated to monitor levodopa at concentrations as low as 0.5 µM in human serum. Furthermore, it has also been verified to be able to distinguish levodopa from other representative substances in the body. Therefore, its performance was more sensitive and rapid than electrochemical workstation. With these advantages, the system can be used in the field of point-of-care testing (POCT) to detect levodopa and provide the possibility to solve clinical demand for levodopa detection.
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