Cheng Ningtao, Chen Dajing, Lou Bin, Fu Jing, Wang Hongyang
State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, People's Republic of China.
School of Medicine, Hangzhou Normal University, Hangzhou, Zhejiang, 311121, People's Republic of China.
Biosens Bioelectron. 2021 Aug 15;186:113246. doi: 10.1016/j.bios.2021.113246. Epub 2021 Apr 20.
Direct serological detection, due to its clinical facility and testing economy, affords prominent clinical values to the early detection of cancer. Surface-enhanced Raman spectroscopy (SERS)-based sensors have shown great promise in realizing this form of detection. Detecting liver cancer early with such a form, especially in terms of monitoring the pathogenic progression from hepatic inflammations to cancer, is the most effective clinical path to reducing the mortality rate. However, the methodology investigation for this purpose remains a formidable challenge. We fabricated a SERS-based sensor, consisting of Au-Ag nanocomplex-decorated ZnO nanopillars on paper. The sensor has an analytic enhancement factor of 1.02 × 10, which is enough to sense the biomolecular information of liver diseases through direct serum SERS analysis. A convolutional neural network (CNN) classifier for recognizing serum SERS spectra was constructed by deep learning. Integrating this sensor with the CNN, we established an intelligent biosensing method and realized direct serological detection of liver diseases within 1 min. As a proof-of-concept, the method achieved a prediction accuracy of 97.78% on an independent test dataset randomly sampled from 30 normal controls, 30 hepatocellular carcinoma (HCC) cases, and 30 hepatitis B (HB) patients. The results suggest this method can be developed for detecting liver diseases clinically and is worthy of exploration as a means of liver cancer surveillance. The presented sensor holds potential for clinical translation to the direct serological detection of diseases.
直接血清学检测因其临床便利性和检测经济性，在癌症早期检测中具有突出的临床价值。基于表面增强拉曼光谱（SERS）的传感器在实现这种检测形式方面已显示出巨大潜力。采用这种形式早期检测肝癌，尤其是在监测从肝脏炎症到癌症的致病进程方面，是降低死亡率最有效的临床途径。然而，为此目的的方法学研究仍然是一项艰巨的挑战。我们制备了一种基于SERS的传感器，它由纸基上装饰有金 - 银纳米复合物的氧化锌纳米柱组成。该传感器的分析增强因子为1.02×10，足以通过直接血清SERS分析来感知肝脏疾病的生物分子信息。通过深度学习构建了用于识别血清SERS光谱的卷积神经网络（CNN）分类器。将该传感器与CNN相结合，我们建立了一种智能生物传感方法，并在1分钟内实现了肝脏疾病的直接血清学检测。作为概念验证，该方法在从30名正常对照、30例肝细胞癌（HCC）病例和30例乙型肝炎（HB）患者中随机抽取的独立测试数据集上实现了97.78%的预测准确率。结果表明，该方法可用于临床检测肝脏疾病，作为肝癌监测手段值得探索。所展示的传感器在临床转化为疾病的直接血清学检测方面具有潜力。
