Abstract-Effect of inhibition on apoptosis of bEnd.3 cells induced by terahertz radiation

Pengfei Xie, Xingxing Lu, Yiwen Sun,

https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10826/108260N/Effect-of-inhibition-on-apoptosis-of-bEnd3-cells-induced-by/10.1117/12.2500304.full

The use of terahertz radiation in various fields such as biology and medicine is increasing every year. Meanwhile, people are increasingly concerned about the mechanism on the interaction between terahertz radiation and biological system. In this study, we evaluated the effect of the cellular response of bEnd.3 which exposing to the terahertz radiation in the range of 0.1-3.5 THz. We collected the spectral data of cells during the irradiation with a temperature of 20.6°C and a relative humidity of 5%. Meanwhile, the apoptosis of cell was assessed by Annexin V-FITC/PI kit. As a result, the apoptosis of cells were inhibited from 0 and 6 hours after terahertz irradiation and promoted at 12 hours after irradiation. Moreover, we also calculated the complex dielectric constant of bEnd.3 cells at different exposures. The results demonstrate that the dielectric loss of cells showed a slight decrease with the increase of exposure in the range of 0.2-1.4 THz.

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Abstract-Quantitative characterization of bovine serum albumin thin-films using terahertz spectroscopy and machine learning methods

Yiwen Sun, Pengju Du, Xingxing Lu, Pengfei Xie, Zhengfang Qian, Shuting Fan, and Zexuan Zhu

https://www.osapublishing.org/boe/abstract.cfm?uri=boe-9-7-2917

The development of new spectral analysis methods in bio thin-film detection has generated intense interest in terahertz (THz) spectroscopy and its application in a wide range of fields. In this paper, it is the first time that machine learning methods are applied to the quantitative characterization of bovine serum albumin (BSA) deposited thin-films detected by terahertz time-domain spectroscopy. The spectra data of BSA thin-films prepared by solutions with concentrations ranging from 0.5 to 35 mg/ml are analyzed using the support vector regression method to learn the underlying model of the frequency against the target concentration. The learned mode successfully predicts the concentrations of the unknown test samples with a coefficient of determination R2 = 0.97932. Furthermore, aiming to identify the relevance of each frequency to the concentration, the maximal information coefficient statistical analysis is used and the three most discriminating frequencies in THz frequency are identified at 1.2, 1.1 and 0.5 THz respectively, which means a good prediction for BSA concentration can be achieved by using the top three relevant frequencies. Moreover, the top discriminating frequencies are in good agreement with the frequencies predicted by a long-wavelength elastic vibration model for BSA protein.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement