Initialising ...
Initialising ...
Initialising ...
Initialising ...
Initialising ...
Initialising ...
Initialising ...
Urano, Kenta; Ito, Atsushi*; Takano, Yuki*; Ouchi, Fumihisa*; Hirayama, Ryoichi*; Furusawa, Yoshiya*; Funayama, Tomoo*; Yokota, Yuichiro*
no journal, ,
To evaluate a role of low LET penumbra region on the biological effects of heavy ions, we have been developing an method for visualizing penumbra using immunofluorescence staining of OH radical-induced guanine damage, 8-OHdG, which was induced on a thin DNA sheet by heavy-ion exposure under aqueous environment. In the last annual meeting, we reported LET and ion species dependence of penumbra area and suggested the increase of penumbra area with increasing LET and particle atomic number. However, the LET dependence seemed to be inconsistent with the theoretical study by Chatterjee and Schaefer. In the present study, we improved image analysis method by adding the reduction procedure of fluorescence background noise.
Kanzaki, Norie; Sakoda, Akihiro; Kataoka, Takahiro*; Tanaka, Hiroshi; Yamaoka, Kiyonori*
no journal, ,
The present study analyzed the data on metabolomics using machine learning, i.e. self-organizing maps (SOM), which allows one to make a comprehensive interpretation of data on biological effects of low-dose radiation. Of 55 metabolites in mouse brain of interest, 27 were detected. For example, Methionine was significantly higher than those in the control group. However, any metabolites indicated no correlations with the exposure mount. We then tried to comprehensively evaluate a set of metabolites. The visible output map that reflected the information of all metabolites was obtained by converting the 27-dimensional data on the metabolites to two dimensions using SOM. This inclusive consideration for all metabolites detected might present that the biological effect depended on the exposure amount. This map may be a representation of the nonlinear phenomenon caused by irradiation and could contribute to supporting discussion about adaptive response of radon.
Shimada, Mikio*; Kanzaki, Norie; Yanagihara, Hiromi*; Miyake, Tomoko*; Matsumoto, Yoshihisa*
no journal, ,
Although mutation frequency depends on organ cell types and differentiation level, it is not fully understood that organ cell types dependent mutation frequency in human cells. In this study, we aimed to establish measurement system of radiation dependent mutation frequency for analyze radiation effect to the human body. For this purpose, we derived four different organ cells such as neural cells, skin keratinocytes, heart muscle cells and blood cells from hiPSCs. Further, using artificial intelligence technology and machine leaning method, we w analyzed differences of mutation frequency during samples.
Sakoda, Akihiro; Ishimori, Yuu; Kanzaki, Norie; Tanaka, Hiroshi; Yamaoka, Kiyonori*; Kataoka, Takahiro*; Mitsunobu, Fumihiro*
no journal, ,
In general, the pathway of so-called radon exposure means the inhalation of its short-lived progeny rather than radon itself. It is also recognized that other pathways like skin permeability of radon should be considered in some special situations. The present study focused on the deposition of radon progeny onto skin surface. The aim of it was to (1) develop a numerical model of the deposition behavior, (2) quantify a key parameter (i.e. deposition velocity) from empirical human data, and (3) compute radiation doses to given target cells in specific exposure scenarios. First, two human data were collected; one was the case of exposure in air (Eatough et al., 1999), and the other was in thermal water (Tempfer et al., 2010). Although there were exposure conditions unknown but necessary for the numerical analysis (e.g. individual activity concentrations of radon progeny, unattached fraction, desorption factor), possible assumptions were made and how greatly those assumptions influence the estimate of deposition velocity was also scrutinized. The deposition velocity in air was found to be higher than that in water. In the case of the exposure in air, the deposition velocity quantified was sensitive to the unattached fraction that must be assumed. Further, skin doses to a basal cell layer and Langerhans cell layer were calculated using the data obtained above. The variability of the doses in air that originated from the assumption of unknown parameters was larger than that in water, as expected from the aforementioned result.