Abstract：
Systemic lupus erythematosus (SLE) is an autoimmune disease with multiple symptoms, and its rapid screening is the research focus of surface-enhanced Raman scattering (SERS) technology. In this study, gold@silver-porous silicon (Au@Ag-PSi) composite substrates were synthesized by electrochemical etching and in-situ reduction methods, which showed excellent sensitivity and accuracy in the detection of rhodamine 6G (R6G) and serum from SLE patients. SERS technology was combined with deep learning algorithms to model serum features using selected CNN, AlexNet, and RF models. 92 % accuracy was achieved in classifying SLE patients by CNN models, and the reliability of these models in accurately identifying sera was verified by ROC curve analysis. This study highlights the great potential of Au@Ag-PSi substrate in SERS detection and introduces a novel deep learning approach for SERS for accurate screening of SLE. The proposed method and composite substrate provide significant value for rapid, accurate, and noninvasive SLE screening and provide insights into SERS-based diagnostic techniques.
摘要：
系统性红斑狼疮（SLE）是一种具有多种症状的自身免疫性疾病，其快速筛选是表面增强拉曼散射(SERS)技术的研究热点。在这项研究中,通过电化学刻蚀和原位还原法合成了金@银-多孔硅（Au@Ag-PSi）复合衬底，在检测SLE患者罗丹明6G（R6G）和血清中显示出优异的灵敏度和准确性。SERS技术与深度学习算法相结合，利用选定的CNN对血清特征进行建模，AlexNet,和RF模型。通过CNN模型对SLE患者进行分类的准确率达到92%，并通过ROC曲线分析验证了这些模型在准确识别血清中的可靠性。这项研究强调了Au@Ag-PSi底物在SERS检测中的巨大潜力，并引入了一种新的SERS深度学习方法来准确筛查SLE。所提出的方法和复合基底为快速、准确,和非侵入性SLE筛查，并提供对基于SERS的诊断技术的见解。
