Abstract：
Effective diagnostic tools for screening of latent tuberculosis infection (LTBI) are lacking. We aim to investigate the performance of LTBI diagnostic approaches using label-free surface-enhanced Raman spectroscopy (SERS). We used 1000 plasma samples from Northeast Thailand. Fifty percent of the samples had tested positive in the interferon-gamma release assay (IGRA) and 50 % negative. The SERS investigations were performed on individually prepared protein specimens using the Raman-mapping technique over a 7 × 7 grid area under measurement conditions that took under 10 min to complete. The machine-learning analysis approaches were optimized for the best diagnostic performance. We found that the SERS sensors provide 81 % accuracy according to train-test split analysis and 75 % for LOOCV analysis from all samples, regardless of the batch-to-batch variation of the sample sets and SERS chip. The accuracy increased to 93 % when the logistic regression model was used to analyze the last three batches of samples, following optimization of the sample collection, SERS chips, and database. We demonstrated that SERS analysis with machine learning is a potential diagnostic tool for LTBI screening.
摘要：
缺乏用于筛查潜伏性结核感染（LTBI）的有效诊断工具。我们旨在研究使用无标记表面增强拉曼光谱（SERS）的LTBI诊断方法的性能。我们使用了来自泰国东北部的1000份血浆样本。50%的样品在干扰素-γ释放测定(IGRA)中测试为阳性,50%为阴性。在需要10分钟才能完成的测量条件下，使用拉曼作图技术在7×7网格区域上对单独制备的蛋白质标本进行SERS研究。机器学习分析方法进行了优化，以获得最佳的诊断性能。我们发现，SERS传感器根据训练测试拆分分析提供81%的准确度，对于所有样本的LOOCV分析提供75%的准确度。无论样品集和SERS芯片的批次间差异如何。当使用logistic回归模型分析最后三批样本时，准确率提高到93%，在优化样本收集后，SERS芯片,和数据库。我们证明了使用机器学习的SERS分析是LTBI筛查的潜在诊断工具。
