• The Korean Journal of Nuclear Medicine Technology
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• v.25 no.1
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• pp.21-28
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• 2021

Purpose Brown fat, or brown adipose tissue (BAT), is involved in non-shivering thermogenesis and creates heat through glucose metabolism. BAT activation occurs stochastically by internal factors such as age, sex, and body mass index (BMI) and external factors such as temperature and environment. In this study, as a retrospective, electronic medical record (EMR) observation study, statistical analysis is conducted to confirm BAT activation and various factors. Materials and Methods From January 2018 to December 2019, EMR of patients who underwent PET/CT scan at the National Cancer Center for two years were collected, a total of 9155 patients were extracted, and 13442 case data including duplicate scan were targeted. After performing a univariable logistic regression analysis to determine whether BAT activation is affected by the environment (outdoor temperature) and the patient's condition (BMI, cancer type, sex, and age), A multivariable regression model that affects BAT activation was finally analyzed by selecting univariable factors with P<0.1. Results BAT activation occurred in 93 cases (0.7%). According to the results of univariable logistic regression analysis, the likelihood of BAT activation was increased in patients under 50 years old (P<0.001), in females (P<0.001), in lower outdoor temperature below 14.5℃ (P<0.001), in lower BMI (P<0.001) and in patients who had a injection before 12:30 PM (P<0.001). It decreased in higher BMI (P<0.001) and in patients diagnosed with lung cancer (P<0.05) In multivariable results, BAT activation was significantly increased in patients under 50 years (P<0.001), in females (P<0.001) and in lower outdoor temperature below 14.5℃ (P<0.001). It was significantly decreased in higher BMI (P<0.05). Conclusion A retrospective study of factors affecting BAT activation in patients who underwent PET/CT scan for 2 years at the National Cancer Center was conducted. The results confirmed that BAT was significantly activated in normal-weight women under 50 years old who underwent PET/CT scan in weather with an outdoor temperature of less than 14.5℃. Based on this result, the patient applied to the factor can be identified in advance, and it is thought that it will help to reduce BAT activation through several studies in the future.

• Journal of Technology Innovation
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• v.17 no.1
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• pp.1-48
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• 2009

Recently we have found some symptoms that R&D fiscal incentives might not work well what it has intended through the analysis of current statistics of firm's R&D data. Firstly, we found that the growth rate of R&D investment in private sector during the recent decade has been slowdown. The average of growth rate (real value) of R&D investment is 7.1% from 1998 to 2005, while it was 13.9% from 1980 to 1997. Secondly, the relative share of R&D investment of SME has been decreased to 21%('05) from 29%('01), even though the tax credit for SME has been more beneficial than large size firm, Thirdly, The R&D expenditure of large size firms (besides 3 leading firms) has not been increased since late of 1990s. We need to find some evidence whether fiscal incentives are effective in increasing firm's R&D investment. To analyse econometric model we use firm level unbalanced panel data for 4 years (from 2002 to 2005) derived from MOST database compiled from the annual survey, "Report on the Survey of Research and Development in Science and Technology". Also we use fixed effect model (Hausman test results accept fixed effect model with 1% of significant level) and estimate the model for all firms, large firms and SME respectively. We have following results from the analysis of econometric model. For large firm: i ) R&D investment responds elastically (1.20) to sales volume. ii) government R&D subsidy induces R&D investment (0.03) not so effectively. iii) Tax price elasticity is almost unity (-0.99). iv) For large firm tax incentive is more effective than R&D subsidy For SME: i ) Sales volume increase R&D investment of SME (0.043) not so effectively. ii ) government R&D subsidy is crowding out R&D investment of SME not seriously (-0.0079) iii) Tax price elasticity is very inelastic (-0.054) To compare with other studies, Koga(2003) has a similar result of tax price elasticity for Japanese firm (-1.0036), Hall((l992) has a unit tax price elasticity, Bloom et al. (2002) has $-0.354{\sim}-0.124$ in the short run. From the results of our analysis we recommend that government R&D subsidy has to focus on such an areas like basic research and public sector (defense, energy, health etc.) not overlapped private R&D sector. For SME government has to focus on establishing R&D infrastructure. To promote tax incentive policy, we need to strengthen the tax incentive scheme for large size firm's R&D investment. We recommend tax credit for large size film be extended to total volume of R&D investment.