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JaeWoo Park,KeeHwan Park,KeunHo Joe,SookHee Choi,InJae Lee,JuHee Hwang,Min Kwon,ShengMin Wang,DaiJin Kim 대한신경정신의학회 2012 PSYCHIATRY INVESTIGATION Vol.9 No.3
Objective-The purpose of the study was to develop the Korean version of the Stage of Change Readiness and Treatment Eagerness Scale for Smoking Cessation (K-SOCRATES-S) based on the Korean version of the Stages of Readiness for Change and Eagerness for Treatment scale (K-SOCRATES). This paper also demonstrates its reliability and validity among patients with nicotine dependence in South Korea. Methods-At seven healthcare promotion centers in Gyeonggi-do, 333 male smokers aged 20 to 70 who visited smoking cessation clinic were recruited for this study and the K-SOCRATES-S was administered. After three months, the number of respondents who successfully stopped smoking was assessed by testing their urine cotinine level. Subsequently, exploratory factor analysis was performed to verify the reliability and validity of the K-SOCRATES-S. Also, a logistic regression analysis was performed to examine the variables that can predict the successful cessation of smoking on subscales of the K-SOCRATES-S. Results-Exploratory factor analysis of the K-SOCRATES-S showed that the scale consisted of three factors: Taking Steps, Recognition, and Ambivalence. The scales measuring Taking Steps and Recognition in this scale had a significantly positive correlation with the scores observed on Kim’s smoking cessation motivation scale. The scales measuring Taking Steps and Recognition had a significantly negative correlation with Ambivalence. Overall, the results indicate that the K-SOCRATES-K scale showed high validity. Conclusion-The K-SOCRATES-S developed in the present study is highly reliable and valid for predicting a patient’s likelihood of success in quitting smoking among patients who want to cease smoking.
Effects of Smoking Cessation on Gene Expression in Human Leukocytes of Chronic Smoker
SooJeong Kim,SuYoung Kim,JaeHwa Kim,DaiJin Kim 대한신경정신의학회 2014 PSYCHIATRY INVESTIGATION Vol.11 No.3
Objective-The risks of cigarette smoking concerning higher systemic disease mortality are lessened by smoking cessation. Methods-Microarray analysis compared the expression profiles of smokers who were successful and not successful at smoking cessation, with the goal of identifying genes that might serve as potential biomarkers or that might be valuable in elucidating distinct biological mechanisms. The mRNAs were isolated and compared from peripheral leukocytes of six smokers who were successful in cessation and six smokers who failed in smoking cessation. Results-Two hundred ninety nine genes displayed significantly different expression; 196 genes were up-regulated and 103 genes were down-regulated in the success group compared to the failure group. Twenty four of these genes were identified with biological processes including immunity, cytoskeleton and cell growth/cycle. Real-time PCR confirmed the differential gene expression. The mRNA levels of HEPACAM family member 2 (HEPACAM2) and tropomodulin 1 (TMOD1) were significantly more expressed in the success group, while the mRNA ubiquitin specific peptides 18 (USP18) were significantly less expressed in the success group compared to the failure group. Conclusion-The results suggest that smoking cessation can modulate cell adhesion and immune response by regulating expression levels of genes, especially HEPACAM2, TMOD1 and USP18, which have an important relationship with smoking cessation.
Daijin Kim,In-Hyun Cho 한국정보과학회 1998 Journal of Electrical Engineering and Information Vol.3 No.6
This paper concerns an improvement of the approximation accuracy in the FBF (Fuzzy Basis Function)-based fuzzy system. The improvement is resulted from using the optimal values of input-output model parameters in the series expansions of fuzzy basis functions, where the optimal values are obtained from learning the model parameters by the given training samples. The learning procedure is performed in the alternately manner that the model parameters of IF parts are updated by one LMS (Least Mean Square) method under the fixed THEN part's model parameters and the model parameters of THEN parts are then updated by another LMS method under the fixed IF part's model parameters. These alternative adjustments of model parameters are continued until the mean squared error (MSE) is not reduced any more. We can reduce the MSE further by allowing the FBFs to have different left and right variance values at both sides. The proposed optimized FBF-based fuzzy system is applied to approximate the Mackey-Glass chaotic time-series and the simulation results show that it outperforms the non-trained FBF-based fuzzy system by reducing the MSE to the half approximately.