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RISS 인기검색어
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- 저자 : ( Bossng Kang ) (검색결과 4 건)
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( Bossng Kang ),( Chae Young Bang ),( Se Young Choung ),( Kyungwoo Choi ),( Changsun Kim ),( Yaejin Hutchison ),( Hyuk Joong Choi ) 대한피부과학회 2015 대한피부과학회 학술발표대회집 Vol.67 No.2
Background: Aldehyde dehydrogenase 2 (ALDH 2) metabolizes acetaldehyde, the major cause of alcohol hangover symptoms. It also detoxifies endogenouscytotoxic aldehydes, such as 4-hydroxynonenal. Oxidative stress promotes lipid peroxidation of cellular membrane, leading to accumulation of reactive aldehydes that contribute to itch signaling via mast cell degranulation and activation of TRPA1 on sensory neuron. A variety of anti-hangover products are available, however, almost none of them has been proven to show enhanced metabolizing capacity of ALDH 2 in a live subject. Objectives: We aimed to test a specific product of interest. Methods: A powder sample of anti-hangover product (KISLip, Pico Entech, Korea) was examined by in vitro & in vivo experiments to measure the amount of NADH formation, generated through catalytic conversion of acetaldehyde. In-vivo examination tested the ethanol and acetaldehyde level in blood of rats with oral infusion of substance before or after ethanol intake. Results: The activities of alcohol dehydrogenase & aldehyde dehydrogenase within the anti-hangover substance were 1.84 unit/g and 0.28 unit/g. The oxidation capacities in rats were dose-dependently increased after substance gavages. Particularly, the cases with oral intake of substance 220 mg/kg after 1hr of ethanol intake have shown more meaningful decreases in acetaldehyde level in blood. Conclusion: Oral intake of anti-hangover substance potentially enhanced ALDH 2 capacity within circulation
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( Bossng Kang ),( Chae Young Bang ),( Se Young Choung ),( Heung Taek Kwon ),( Kyungwoo Choi ),( Changsun Kim ),( Yaejin Hutchison ),( Hyuk Joong Choi ) 대한피부과학회 2015 대한피부과학회 학술발표대회집 Vol.67 No.2
Background: Aldehyde dehydrogenase 2 (ALDH 2) metabolizes acetaldehyde, the major cause of alcohol hangover symptoms. It also detoxifies other endogenous aldehydes, such as 2-nonenal. It is an unsaturated aldehyde with an unpleasant greasy and grassy odor. Oxidative stress promotes peroxidation of polyunsaturated fatty acids in cellular membrane, generating 2-nonenal that contribute to aging body odor. A variety of anti-hangover products are available, however, almost none of them has been proven to show enhanced metabolizing capacity of ALDH 2 in a live subject. Objectives: We aimed to test a specific product of interest. Methods: A powder sample of anti-hangover product(KISLip, Pico Entech, Korea) was examined by in vitro & in vivo experiments to measure the amount of NADH formation, generated through catalytic conversion of acetaldehyde. In-vivo examination tested the ethanol and acetaldehyde level in blood of rats with oral infusion of substance before or after ethanol intake. Results: The activities of alcohol dehydrogenase & aldehyde dehydrogenase within the anti-hangover substance were 1.84 unit/g and 0.28 unit/g. The oxidation capacities in experimental rats were dose-dependentlyincreased after substance gavages. Particularly, the cases with oral intake of substance 220 mg/kg after 1hr of ethanol intake have shown more meaningful decreases in acetaldehyde level in blood. Conclusion: Oral intake of anti-hangover substance potentially enhance ALDH 2 capacity within circulation.
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Screening Adolescent Suicidal Behavior and Risk Factors: A Machine-Learning Approach
Seon-Hi Shin,Bossng Kang,Myung-Suk Woo,Youn-Ju Park 한국컴퓨터교육학회 2024 컴퓨터교육학회 논문지 Vol.27 No.5
This study aimed to create machine learning-based screening tools for middle school students to predict suicide planning or attempts in school settings, and to identify the risk factors. A total of 3,812 records from a national database established through rigorous sampling designs, the 2022 Korean Youth Risk Behavior Web-based Survey (KYRBWS), were used to ensure high generalizability and minimize bias of the study’s outcomes. Five machine-learning models, utilizing various algorithms available in SAS or Python, underwent training with a 10-fold validation process. Performance evaluation included metrics such as accuracy, sensitivity, specificity, and AUC, resulting from using a common unseen test dataset. Feature importance was interpreted through the average absolute values of Shapley additive explanations (SHAP) as well as the standardized regression coefficients. The artificial neural-network model with three hidden layers and dropout layers between each dense layer emerged as the top performer. It achieved sensitivity and AUC exceeding 91%, with accuracy and specificity at 86%. However, all five models, utilizing an optimal set of nine features, demonstrated strong performance. Mental health features such as suicidal thoughts, feeling lonely, or feeling sad, and the subjective perception of dental health emerged as significant risk factors. Adolescents considering suicide were approximately 12.7 times more likely to make plans or attempt suicide than their peers without such ideation (OR = 12.7; 95% CI = [8.0-20.1]). Individuals perceiving dental health as Very bad were 4.4 times more prone to suicidal behavior than those with Average ratings (OR = 4.4; 95% CI = [1.4-13.5]). Those always feeling lonely were 2.8 times more likely to engage in suicidal behavior (OR = 2.8; 95% CI = [1.1-7.1]), while persistent feelings of sadness increased the likelihood by 1.8 times (OR = 1.8; 95% CI = [1.2-2.8]). The study also offers valuable guidance on algorithm selection and suitable options for similar applications.
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