Literature DB >> 33823401

Characterization and discrimination of human colorectal cancer cells using terahertz spectroscopy.

Yuqi Cao1, Jiani Chen2, Guangxin Zhang3, Shuyu Fan1, Weiting Ge2, Wangxiong Hu2, Pingjie Huang1, Dibo Hou1, Shu Zheng4.   

Abstract

Terahertz technology has been widely used in biomedical research. Herein, terahertz time-domain attenuated total reflection (THz TD-ATR) spectroscopy was employed to characterize and discriminate human cancer cell lines (DLD-1 and HT-29). Terahertz responses of the cell lines were measured and Savitzky-Golay algorithm was applied to smooth the spectra of refractive index, absorption coefficient and dielectric loss tangent in terahertz regime. Principal component analysis (PCA) was then adopted for feature extraction and cell characterization. Based on the processed data, cancer cell lines were discriminated by applying random forests (RF) method to analyze three characteristic parameters separately and the results from them were compared. Results indicate that absorption coefficient was the most sensitive parameter for cancer cell discrimination. Our study suggests great potential for human cancer cell recognition and provides experimental basis for liquid biopsy.
Copyright © 2021. Published by Elsevier B.V.

Entities:  

Keywords:  Cancer cells discrimination; DLD-1 cell line; HT-29 cell line; Principal component analysis; Terahertz time-domain attenuated total reflection spectroscopy

Year:  2021        PMID: 33823401     DOI: 10.1016/j.saa.2021.119713

Source DB:  PubMed          Journal:  Spectrochim Acta A Mol Biomol Spectrosc        ISSN: 1386-1425            Impact factor:   4.098


  2 in total

1.  Terahertz anisotropy in fascia and lean meat tissues.

Authors:  Hongting Xiong; Hongyan Sun; Jiangping Zhou; Haotian Li; Hao Zhang; Shaojie Liu; Jiahua Cai; Lin Feng; Jungang Miao; Sai Chen; Xiaojun Wu
Journal:  Biomed Opt Express       Date:  2022-04-04       Impact factor: 3.562

2.  THz-ATR Spectroscopy Integrated with Species Recognition Based on Multi-Classifier Voting for Automated Clinical Microbial Identification.

Authors:  Wenjing Yu; Jia Shi; Guorong Huang; Jie Zhou; Xinyu Zhan; Zekang Guo; Huiyan Tian; Fengxin Xie; Xiang Yang; Weiling Fu
Journal:  Biosensors (Basel)       Date:  2022-05-31
  2 in total

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