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Identification of Catecholamine Neurotransmitters Using a Fluorescent Electronic Tongue Publisher Pubmed



Jafarinejad S1 ; Bigdeli A2, 3 ; Ghazikhansari M4 ; Sasanpour P1, 5 ; Hormozinezhad MR2
Authors
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Authors Affiliations
  1. 1. Department of Medical Physics and Biomedical Engineering, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, 19857-17443, Iran
  2. 2. Department of Chemistry, Sharif University of Technology, Tehran, 11155-9516, Iran
  3. 3. Institute for Nanoscience and Nanotechnology, Sharif University of Technology, Tehran, 14588-89694, Iran
  4. 4. Department of Pharmacology, School of Medicine, Tehran University of Medical Sciences, P.O. Box 13145-784, Tehran, 14176-13151, Iran
  5. 5. School of Nanoscience, Institute for Research in Fundamental Sciences (IPM), P.O. Box 19395-5531, Tehran, Iran

Source: ACS Chemical Neuroscience Published:2020


Abstract

Catecholamine neurotransmitters, specifically, dopamine (DA), epinephrine (EP), and norepinephrine (NE), are known as substantial indicators of various neurological diseases. Developing rapid detection methods capable of simultaneously screening their concentrations is highly desired for early clinical diagnosis of such diseases. To this aim, we have designed an optical sensor array using three fluorescent dyes with distinct emission bands and have monitored variations in their emission profiles upon the addition of DA, EP, and NE in the presence of gold ions. Because of the different reducing power of catecholamines, differently sized gold nanoparticles (GNPs) with different levels of aggregation were generated, resulting in different amounts of spectral overlap between the absorption band of the in situ generated plasmonic GNPs and the emission bands of the fluorescent dyes. These energy-transfer-based fingerprint profiles were used to discriminate the neurotransmitters by applying pattern recognition methods including linear discriminant analysis (LDA) and artificial neural networks (ANN) and to determine their concentration using multiple linear regression (MLR). Our proposed array also showed a good performance in the discrimination of DA, EP, and NE in complex biological media such as human urine. © 2019 American Chemical Society.