Department of Medical Physics and Biomedical Engineering, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Department of Pharmacology, School of Medicine, Tehran University of Medical Sciences, P.O. Box, 13145-784, Tehran, Iran.
Sci Rep. 2017 Aug 15;7(1):8266. doi: 10.1038/s41598-017-08704-5.
Catecholamine neurotransmitters, generally including dopamine (DA), epinephrine (EP) and norepinephrine (NE) are known as substantial indicators of various neurological diseases. Simultaneous detection of these compounds and their metabolites is highly recommended in early clinical diagnosis. To this aim, in the present contribution, a high performance colorimetric sensor array has been proposed for the detection and discrimination of catecholamines based on their reducing ability to deposit silver on the surface of gold nanorods (AuNRs). The amassed silver nanoshell led to a blue shift in the longitudinal localized surface plasmon resonance (LSPR) peak of AuNRs, creating a unique pattern for each of the neurotransmitters. Hierarchical cluster analysis (HCA) and linear discriminate analysis (LDA) pattern recognition techniques were employed to identify DA, EP and NE. The proposed colorimetric array is able to differentiate among individual neurotransmitters as well as their mixtures, successfully. Finally, it was shown that the sensor array can identify these neurotransmitters in human urine samples.
儿茶酚胺神经递质,一般包括多巴胺(DA)、肾上腺素(EP)和去甲肾上腺素(NE),被认为是各种神经疾病的重要指标。因此,在早期临床诊断中,强烈建议同时检测这些化合物及其代谢物。为此,在本研究中,我们提出了一种基于儿茶酚胺还原能力在金纳米棒(AuNRs)表面沉积银的高性能比色传感器阵列,用于检测和区分儿茶酚胺。聚集的银纳米壳导致 AuNRs 的纵向局域表面等离子体共振(LSPR)峰蓝移,为每种神经递质创造了独特的模式。采用层次聚类分析(HCA)和线性判别分析(LDA)模式识别技术来识别 DA、EP 和 NE。该比色阵列能够成功地区分单个神经递质及其混合物。最后,证明该传感器阵列可以识别人尿液样本中的这些神经递质。