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采用单金纳米颗粒光子爆发计数技术的均相免疫分析。

Homogeneous immunoassays by using photon burst counting technique of single gold nanoparticles.

作者信息

Lan Tao, Wang Jinjie, Dong Chaoqing, Huang Xiangyi, Ren Jicun

机构信息

School of Chemistry & Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, PR China.

School of Chemistry & Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, PR China.

出版信息

Talanta. 2015 Jan;132:698-704. doi: 10.1016/j.talanta.2014.10.004. Epub 2014 Oct 23.

Abstract

In this paper, we reported a sensitive single particle method by combining the photon burst counting technique with gold nanoparticles (GNPs) as labeling probes. The photon bursting of single GNPs will be generated in a highly focused laser beam (less than 1 fL) due to the plasmon resonance scattering and Brownian motion of GNPs. We observed an excellent linear relationship between the photon burst counts and the number of particles in GNPs solution. We investigated the statistical behaviors of background noise and photon burst signal of GNPs, and proposed the data processing method based on Gaussian distribution of the background noise. A new homogeneous sandwich immunoassay was developed by using this single particle method. We evaluated the performance of this method by using prostate-specific antigen (PSA) as a model. The linear range of PSA was 1-1000 pmol/L and the detection limit was 0.8 pmol/L. This novel method was successfully used for the direct detection of cancer biomarker PSA in human serum samples. Our results were in good agreement with conventional ELISA assays.

摘要

在本文中,我们报道了一种灵敏的单粒子方法,该方法将光子爆发计数技术与作为标记探针的金纳米颗粒(GNPs)相结合。由于GNPs的等离子体共振散射和布朗运动,单个GNPs的光子爆发将在高度聚焦的激光束(小于1 fL)中产生。我们观察到光子爆发计数与GNPs溶液中粒子数量之间存在良好的线性关系。我们研究了GNPs背景噪声和光子爆发信号的统计行为,并提出了基于背景噪声高斯分布的数据处理方法。利用这种单粒子方法开发了一种新的均相夹心免疫分析法。我们以前列腺特异性抗原(PSA)为模型评估了该方法的性能。PSA的线性范围为1 - 1000 pmol/L,检测限为0.8 pmol/L。这种新方法成功用于直接检测人血清样本中的癌症生物标志物PSA。我们的结果与传统ELISA分析结果高度一致。

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