Showing posts with label Dibo Hou. Show all posts
Showing posts with label Dibo Hou. Show all posts

Sunday, December 22, 2019

Abstract-Study on glycoprotein terahertz time-domain spectroscopy based on composite multiscale entropy feature extraction method




Author links open overlay panelPingjie HuanZhangwei HuangXiaodong Lu, Yuqi CaoJie Yu,  Dibo Hou,  Guangxin Zhang



https://www.sciencedirect.com/science/article/abs/pii/S1386142519313393

Tumor genesis is accompanied by glycosylation of related proteins. Glycoprotein is usually regarded as a tumor marker since glycoproteins are consumed remarkably more by the cancer cells than the normal ones. In this paper, the terahertz time-domain attenuated total reflection (ATR) technique is applied to inspect the glycoprotein solution from a concentration gradient of 0.2 mg/ml to 50 mg/ml. A significant nonlinear relationship between the absorption coefficient and the concentrations has been discovered. The influence of the dynamical hydration shell around glycoprotein molecules on the absorption coefficient is discussed and the phenomenon is explained by the concepts of THz excess and THz defect. In order to identify glycoproteins, features are obtained by composite multiscale entropy (CMSE) method and clustered by the K-means algorithm. The results indicate that features extracted by the CMSE method are better than the Principal Component Analysis (PCA) method in both specificity and sensitivity of recognition. Meanwhile, the absorption coefficient and dielectric loss angle tangent are more suitable for qualitative identification. Research shows that the CMSE method has important directive significance for analyzing glycoprotein terahertz spectroscopy. And it has the potential for glycoprotein related tumor markers identification using terahertz technology in medical applications.

Monday, September 2, 2019

Abstract-Analysis and inspection techniques for mouse liver injury based on terahertz spectroscopy




Pingjie Huang, Yuqi Cao, Jiani Chen, Weiting Ge, Dibo Hou, and Guangxin Zhang

 Flow chart of injured liver tissue discrimination algorithm based on terahertz spectra.
https://www.osapublishing.org/oe/abstract.cfm?uri=oe-27-18-26014

At present, researchers are exploring biological tissue detection method using terahertz techniques. In this paper, techniques to inspect mouse liver injury by using terahertz spectroscopy were studied. The boxplots were applied to remove abnormal data, and the maximal information coefficient was employed to select crucial features from the absorption coefficient and refractive index spectra. Random Forests and AdaBoost were applied to recognize different levels of liver injury. We found that AdaBoost had better performance on low-level injury classification. This work suggests that terahertz techniques have the potential to detect liver injury at an early stage and evaluate liver treatment strategies.
© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement