基于表面增强拉曼技术的致病菌及其耐药性检测研究进展

随着抗菌药物广泛应用于临床，细菌耐药日益严重。实现快速、高灵敏、准确的细菌及其药物敏感性检测是缓解细菌耐药的关键环节。表面增强拉曼光谱(SERS)具有快速、灵敏、无损等优点，可直接获取分子指纹信息，它已成为一种有效的细菌及其耐药性检测技术。不同种类细菌的分子组成和结构存在差异、抗生素处理前后细菌的特征拉曼信号会发生变化，这为表面增强拉曼光谱技术在致病菌及其耐药性检测中的应用提供了依据。基于分子组成与结构的差异, 结合传统多分类数据分析以及机器学习算法，表面增强拉曼光谱技术可以提供客观的诊断信息。这篇综述回顾了近年来表面增强拉曼光谱技术对于致病菌及其耐药性检测的研究进展, 阐述了当前表面增强拉曼光谱技术应用于致病菌检测面临的问题。首先，讨论了致病菌及其耐药性检测中常用SERS基底的材料和结构：金纳米粒子、银纳米粒子、银包金纳米粒子以及新型纳米材料与纳米粒子结合形成的复合SERS基底。然后，概述了SERS检测中捕获细菌的方法，主要介绍了基于核酸适配体、免疫磁性分离、微流控系统以及静电结合的捕获方法，包括上述捕获方法的原理以及捕获方式，综述了以上捕获方法的研究进展。最后，总结了致病菌SERS光谱的各种数据分析方法，通过光谱预处理，特征提取与分类识别，以及构建致病菌SERS光谱诊断模型，实现致病菌及其耐药性的检测；比较了传统的数据分析方法以及机器学习分析方法，重点介绍了深度学习算法在致病菌及其耐药性SERS检测中的优势与应用。文章也对表面增强拉曼光谱应用于致病菌及其耐药性检测的关键问题进行了讨论，并对基于表面增强拉曼技术的致病菌及其耐药性检测方法进行了展望，以促进表面增强拉曼光谱技术在临床检测中的应用。

Research Progress of Pathogenic Bacteria and Their Drug Resistance Detection Based on Surface Enhanced Raman Technology

With antimicrobial drugs widely used in the clinic, the drug resistance of pathogenic bacteria is becoming more and more serious. Rapid, highly sensitive, and accurate detection of bacteria and their drug susceptibility is the key to alleviating bacterial resistance. Surface-Enhanced Raman Scattering (SERS), can be used to obtain molecular fingerprint information directly and has become an effective detection technology for bacteria and their drug resistance. The molecular composition and structure of different species of bacteria are different, and the characteristic Raman signal of bacteria will also change before and after antibiotic treatment, which provides the basis for the application of SERS for the detection of pathogenic bacteria and their drug resistance. Based on the differences in molecular composition and structure, combined with traditional multi-class data analysis and machine learning algorithms, SERS can provide objective diagnostic information. In this review, the research progress of SERS for the detection of pathogenic bacteria and their drug resistance in recent years is reviewed, and the current problems existing in the application of SERS in the detection of pathogenic bacteria are also described. Firstly, the materials and structures of SERS substrates commonly used to detect pathogenic bacteria and their drug resistance were discussed, including gold nanoparticles, silver nanoparticles, silver-coated gold nanoparticles and composite SERS substrates formed by the combination of new nanomaterials and nanoparticles. Then, the methods of capturing bacteria in SERS detection were summarized, mainly based on nucleic acid aptamers, immunomagnetic separation, microfluidic systems and electrostatic binding. The principles and methods of the above methods were described, and the research progress of the above methods was summarized. Finally, various data analysis methods of SERS spectra of pathogenic bacteria were summarized. Through spectral preprocessing, feature extraction, classification and recognition, and the establishment of a diagnosis model of pathogenic bacteria, the detection of pathogenic bacteria and their drug resistance was realized. The traditional data analysis methods and machine learning methods were compared, and the advantages and applications of deep learning algorithms in the SERS detection of pathogenic bacteria and their drug resistance were introduced. In this paper, the key issues in the application of SERS in the detection of pathogenic bacteria and their drug resistance were also discussed, and the detection methods of pathogenic bacteria and their drug resistance based on SERS prospected, to promote the application of surface-enhanced Raman spectroscopy in clinical detection.