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Label-free diagnosis for colorectal cancer through coffee ring-assisted surface-enhanced Raman spectroscopy on blood serum.
Journal of Biophotonics ( IF 2.0 ) Pub Date : 2020-02-09 , DOI: 10.1002/jbio.201960176 Yan Hong 1 , Yongqiang Li 1 , Libin Huang 2 , Wei He 1 , Shouxu Wang 1 , Chong Wang 1 , Guoyun Zhou 1 , Yuanming Chen 1 , Xin Zhou 1 , Yifeng Huang 3 , Wen Huang 3 , Tianxun Gong 3 , Zongguang Zhou 2
Journal of Biophotonics ( IF 2.0 ) Pub Date : 2020-02-09 , DOI: 10.1002/jbio.201960176 Yan Hong 1 , Yongqiang Li 1 , Libin Huang 2 , Wei He 1 , Shouxu Wang 1 , Chong Wang 1 , Guoyun Zhou 1 , Yuanming Chen 1 , Xin Zhou 1 , Yifeng Huang 3 , Wen Huang 3 , Tianxun Gong 3 , Zongguang Zhou 2
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Surface‐enhanced Raman spectroscopy (SERS) is garnering considerable attention for the swift diagnosis of pathogens and abnormal biological status, that is, cancers. In this work, a simple, fast and inexpensive optical sensing platform is developed by the design of SERS sampling and data analysis. The pretreatment of spectral measurement employed gold nanoparticle colloid mixing with the serum from patients with colorectal cancer (CRC). The droplet of particle‐serum mixture formed coffee‐ring‐like region at the rim, providing strong and stable SERS profiles. The obtained spectra from cancer patients and healthy volunteers were analyzed by unsupervised principal component analysis (PCA) and supervised machine learning model, such as support‐vector machine (SVM), respectively. The results demonstrate that the SVM model provides the superior performance in the classification of CRC diagnosis compared with PCA. In addition, the values of carcinoembryonic antigen from the blood samples were compiled with the corresponding SERS spectra for SVM calculation, yielding improved prediction results.
中文翻译：
通过咖啡环辅助的表面增强拉曼光谱对血清进行无标记诊断大肠癌。
表面增强拉曼光谱（SERS）在迅速诊断病原体和异常生物学状态（即癌症）方面引起了广泛关注。在这项工作中，通过SERS采样和数据分析的设计，开发了一种简单，快速且廉价的光学传感平台。光谱测量的预处理是将金纳米粒子胶体与大肠癌（CRC）患者的血清混合。颗粒-血清混合物的液滴在边缘形成咖啡环状区域，提供了牢固而稳定的SERS轮廓。从癌症患者和健康志愿者获得的光谱分别通过无监督主成分分析（PCA）和有监督的机器学习模型（例如支持向量机（SVM））进行分析。结果表明，与PCA相比，SVM模型在CRC诊断分类中提供了优越的性能。此外，将血样中的癌胚抗原值与相应的SERS谱图进行汇编，以进行SVM计算，从而获得更好的预测结果。
更新日期：2020-02-09
中文翻译：
通过咖啡环辅助的表面增强拉曼光谱对血清进行无标记诊断大肠癌。
表面增强拉曼光谱（SERS）在迅速诊断病原体和异常生物学状态（即癌症）方面引起了广泛关注。在这项工作中，通过SERS采样和数据分析的设计，开发了一种简单，快速且廉价的光学传感平台。光谱测量的预处理是将金纳米粒子胶体与大肠癌（CRC）患者的血清混合。颗粒-血清混合物的液滴在边缘形成咖啡环状区域，提供了牢固而稳定的SERS轮廓。从癌症患者和健康志愿者获得的光谱分别通过无监督主成分分析（PCA）和有监督的机器学习模型（例如支持向量机（SVM））进行分析。结果表明，与PCA相比，SVM模型在CRC诊断分类中提供了优越的性能。此外，将血样中的癌胚抗原值与相应的SERS谱图进行汇编，以进行SVM计算，从而获得更好的预测结果。
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