Dr. Shuo Chen Profile

Dr. Shuo Chen

at National Univ of Defense Technology

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Proceedings Article | 24 November 2021 Paper

Conformational analysis of soluble amyloid β-peptide using Fourier transform infrared spectroscopy with transmission mode

Wenyu Peng, Shuo Chen

Proceedings Volume 12067, 1206705 (2021) https://doi.org/10.1117/12.2603062

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Alzheimer’s disease (AD) is a common neurodegenerative disease characterized by cognitive and memory function impairment. Studies have shown that the amyloid-beta (Aβ) plays an important role in the disease progression. The monomeric Aβ has been associated with biological functions, such as memory, learning, and neuroprotection, while the soluble Aβoligomers are the primary toxic agents and the resultant amyloid fibrils are relatively benign. Thus, the conformation detection of Aβ is quite necessary for AD research. IR spectroscopy is an excellent technique used for protein conformational analysis, for the C=O stretching vibration of the peptide is sensitive to the range of 1600-1700cm^{- 1}. In this research, we proposed a method that detects the conformation of soluble Aβ by using Fourier transform infrared (FTIR) spectroscopy with a transmission approach. Although the attenuated total reflectance (ATR) accessory made from zinc selenide or germanium is the most popular approach for recording liquid spectra of the peptides, the volume requires almost 5 mL of soluble Aβ to cover the crystal to achieve a higher signal. Herein we designed a 25×4 mm calcium fluoride (CaF₂) substrate with the center has a groove with 5μm deep and 10 mm diameter, it just needed 2uL of soluble amyloid β-peptide to fill the groove and also achieved a high transmission signal. Moreover, based on this method, we found that the process of oligomer-to-fibrils transition occurred much faster within the first 24 hours, and the secondary structure changed slowly in the following time of 48-96 h. These results demonstrated that FTIR is an exquisite way to characterize the aggregation process of peptides, it not only economizes the reagents but also enables give an almost continuous structural view of such process.

Proceedings Article | 5 November 2020 Paper

Diagnosis of intraoperative hepatocellular carcinoma based on FTIR vibration spectroscopy using supervised machine learning

Wenyu Peng, Gangqiang Cui, Shuo Chen, Xiaoyun Lu

Proceedings Volume 11566, 1156604 (2020) https://doi.org/10.1117/12.2574177

KEYWORDS: Tissues, FT-IR spectroscopy, Spectroscopy, Liver cancer, Machine learning, Cancer, Absorbance, Surgery, Diagnostics

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Intraoperative diagnosis plays an essential role in cancer surgery by providing fast and accurate information to clinicians to make a decision. The standard workflow for histopathology based intraoperative diagnosis is generally considered to be time-consuming and labor-intensive. Fourier transform infrared (FTIR) vibration spectroscopy technique has been demonstrated to be a useful tool that yields a molecular fingerprint and provides rapid, nondestructive, high-throughput and clinically relevant diagnostic information. In this study, FTIR spectrometer based on synchrotron radiation was applied to collect the IR spectra of the liver cancer tissues and the adjacent non-cancer tissues of hepatocellular carcinoma (HCC) patients. The FTIR data demonstrated that the ratio of 2959/2926cm^-1, 1654/1548cm^-1, 1084/1548cm^-1and 1455/1398cm^-1 had a significant difference between the two groups, which could serve as indicators to distinguish the liver cancer region from the adjacent non-cancer tissue. Then the supervised machine learning techniques including discriminant analysis coupled with principal component analysis (PCA-DA), support vector machines (SVM) and backpropagation neural networks (BPNN) were applied to classify the spectra data. Finally the performance of these models, such as their precision, sensitivity, specificity and accuracy was assessed, and the results have proved that coupling the FTIR vibration spectroscopy with supervised machine learning techniques could be considered as an accurate and efficient method for the intraoperative diagnosis of HCC.

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