Three-step one-way model in terahertz biomedical detection

Yan Peng; Jieli Huang; Jie Luo; Zhangfan Yang; Liping Wang; Xu Wu; Xiaofei Zang; Chen Yu; Min Gu; Qing Hu; Xicheng Zhang; Yiming Zhu; Songlin Zhuang

doi:10.1186/s43074-021-00034-0

Three-step one-way model in terahertz biomedical detection

doi: 10.1186/s43074-021-00034-0

基金项目:

This work is financially supported by NSFC (61922059, 81961138014, 61805140, 81873609, 61771314) and the Terahertz Science and Technology Frontier Project (6198802).

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出版历程
- 收稿日期: 2021-04-19
- 录用日期: 2021-06-11
- 网络出版日期: 2021-07-23

Three-step one-way model in terahertz biomedical detection

1.
Shanghai Key Lab of Modern Optical System, Terahertz Technology Innovation Research Institute, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai, 200093, China
2.
Department of Nephrology, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China
3.
Laboratory of Artificial-Intelligence Nanophotonics, University of Shanghai for Science and Technology, Shanghai, 200093, P
4.
Department of Electrical Engineering and Computer Science and Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
5.
The Institute of Optics, University of Rochester, Rochester, New York, 14627, USA

Funds:

This work is financially supported by NSFC (61922059, 81961138014, 61805140, 81873609, 61771314) and the Terahertz Science and Technology Frontier Project (6198802).

摘要

摘要: Terahertz technology has broad application prospects in biomedical detection. However, the mixed characteristics of actual samples make the terahertz spectrum complex and difficult to distinguish, and there is no practical terahertz detection method for clinical medicine. Here, we propose a three-step one-way terahertz model, presenting a detailed flow analysis of terahertz technology in the biomedical detection of renal fibrosis as an example:1) biomarker determination:screening disease biomarkers and establishing the terahertz spectrum and concentration gradient; 2) mixture interference removal:clearing the interfering signals in the mixture for the biomarker in the animal model and evaluating and retaining the effective characteristic peaks; and 3) individual difference removal:excluding individual interference differences and confirming the final effective terahertz parameters in the human sample. The root mean square error of our model is three orders of magnitude lower than that of the gold standard, with profound implications for the rapid, accurate and early detection of diseases.
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Abstract: Terahertz technology has broad application prospects in biomedical detection. However, the mixed characteristics of actual samples make the terahertz spectrum complex and difficult to distinguish, and there is no practical terahertz detection method for clinical medicine. Here, we propose a three-step one-way terahertz model, presenting a detailed flow analysis of terahertz technology in the biomedical detection of renal fibrosis as an example: 1) biomarker determination: screening disease biomarkers and establishing the terahertz spectrum and concentration gradient; 2) mixture interference removal: clearing the interfering signals in the mixture for the biomarker in the animal model and evaluating and retaining the effective characteristic peaks; and 3) individual difference removal: excluding individual interference differences and confirming the final effective terahertz parameters in the human sample. The root mean square error of our model is three orders of magnitude lower than that of the gold standard, with profound implications for the rapid, accurate and early detection of diseases.
- Terahertz spectroscopy /
- Renal fibrosis /
- Biomedical detection /
- Biomarker /
- Mixture interference /
- Individual difference

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参考文献(43)

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