Identification of Catecholamine Neurotransmitters Using a Fluorescent Electronic Tongue.,ACS Chemical Neuroscience

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Identification of Catecholamine Neurotransmitters Using a Fluorescent Electronic Tongue.
ACS Chemical Neuroscience ( IF 4.1 ) Pub Date : 2019-12-19 , DOI: 10.1021/acschemneuro.9b00537
Somayeh Jafarinejad ₁ , Arafeh Bigdeli _{2,

3} , Mahmoud Ghazi-Khansari ₄ , Pezhman Sasanpour _{1,

5} , M Reza Hormozi-Nezhad ₂

Affiliation

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.

中文翻译：

使用荧光电子舌识别邻苯二酚神经递质。

儿茶酚胺神经递质，特别是多巴胺（DA），肾上腺素（EP）和去甲肾上腺素（NE），是各种神经系统疾病的重要指标。对于此类疾病的早期临床诊断，迫切需要开发能够同时筛选其浓度的快速检测方法。为此，我们设计了一种使用三种具有不同发射带的荧光染料的光学传感器阵列，并在金离子存在下添加DA，EP和NE时监测了其发射曲线的变化。由于儿茶酚胺的还原能力不同，因此生成了具有不同聚集水平的大小不同的金纳米颗粒（GNP），结果导致原位产生的等离激元GNP的吸收带和荧光染料的发射带之间的光谱重叠量不同。这些基于能量转移的指纹图谱通过应用包括线性判别分析（LDA）和人工神经网络（ANN）在内的模式识别方法来区分神经递质，并使用多元线性回归（MLR）确定其浓度。我们提出的阵列在区分复杂的生物介质（如人尿）中的DA，EP和NE方面也表现出良好的性能。这些基于能量转移的指纹图谱通过应用包括线性判别分析（LDA）和人工神经网络（ANN）在内的模式识别方法来区分神经递质，并使用多元线性回归（MLR）确定其浓度。我们提出的阵列在区分复杂的生物介质（如人尿）中的DA，EP和NE方面也表现出良好的性能。这些基于能量转移的指纹图谱通过应用包括线性判别分析（LDA）和人工神经网络（ANN）在内的模式识别方法来区分神经递质，并使用多元线性回归（MLR）确定其浓度。我们提出的阵列在区分复杂的生物介质（如人尿）中的DA，EP和NE方面也表现出良好的性能。

更新日期：2019-12-19

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