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表面等离子体共振生物传感技术的发展趋势:材料、方法和机器学习。

Trends in surface plasmon resonance biosensing: materials, methods, and machine learning.

机构信息

Department of Chemistry, University of California, Riverside, CA, 92521, USA.

出版信息

Anal Bioanal Chem. 2024 Oct;416(24):5221-5232. doi: 10.1007/s00216-024-05367-w. Epub 2024 Jun 6.

DOI:10.1007/s00216-024-05367-w
PMID:38839686
Abstract

Surface plasmon resonance (SPR) proves to be one of the most effective methods of label-free detection and has been integral for the study of biomolecular interactions and the development of biosensors. This trend delves into the latest SPR research and progress built upon the Kretschmann configuration, a pivotal platform, and highlights three key developments that have enhanced the capabilities of the technique. We will first cover a range of explorations of novel plasmonic materials that have shaped SPR performance. Innovative signal transduction and collection, which leverages traditional materials and emerging alternatives, will then be discussed. Finally, the evolving landscape of data analysis, including the integration of machine learning algorithms to navigate complex SPR datasets, will be reviewed. We will also discuss the implementation of these improvements that have enabled new biosensing functions. These advancements not only pave the way for enhanced biosensing in general but also open new avenues for the technique to play a more significant role in research concerning human health.

摘要

表面等离子体共振(SPR)被证明是最有效的无标记检测方法之一,对于研究生物分子相互作用和开发生物传感器至关重要。本趋势探讨了基于 Kretschmann 配置的最新 SPR 研究和进展,该配置是一个关键平台,并强调了增强该技术能力的三个关键发展。我们将首先介绍一系列新型等离子体材料的探索,这些材料塑造了 SPR 的性能。然后将讨论创新的信号转导和收集,利用传统材料和新兴替代品。最后,将回顾数据分析的发展趋势,包括机器学习算法的整合,以处理复杂的 SPR 数据集。我们还将讨论这些改进的实施,这些改进使新的生物传感功能成为可能。这些进展不仅为一般的生物传感铺平了道路,也为该技术在涉及人类健康的研究中发挥更大作用开辟了新途径。

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