Comparative evaluation of the mutagenicity and genotoxicity of smoke condensate derived from Korean cigarettes

OBJECTIVES: Cigarette smoking is associated with carcinogenesis owing to the mutagenic and genotoxic effects of cigarette smoke. The aim of this study was to evaluate the mutagenic and genotoxic effects of Korean cigarettes using in vitro assays. METHODS: We selected 2 types of cigarettes (TL and TW) as benchmark Korean cigarettes for this study, because they represent the greatest level of nicotine and tar contents among Korean cigarettes. Mutagenic potency was expressed as the number of revertants per μg of cigarette smoke condensate (CSC) total particulate matter whereas genotoxic potency was expressed as a concentration-dependent induction factor. The CSC was prepared by the International Organization for Standardization 3308 smoking method. CHO-K1 cells were used in vitro micronucleus (MNvit) and comet assays. Two strains of Salmonella typhimurium (Salmonella enterica subsp.enterica; TA98 and TA1537) were employed in Ames tests. RESULTS: All CSCs showed mutagenicity in the TA98 and TA1537 strains. In addition, DNA damage and micronuclei formation were observed in the comet and MNvit assays owing to CSC exposure. The CSC from the 3R4F Kentucky reference (3R4F) cigarette produced the most severe mutagenic and genotoxic potencies, followed by the CSC from the TL cigarette, whereas the CSC from the TW cigarette produced the least severe mutagenic and genotoxic potencies. CONCLUSIONS: The results of this study suggest that the mutagenic and genotoxic potencies of the TL and TW cigarettes were weaker than those of the 3R4F cigarette. Further study on standardized concepts of toxic equivalents for cigarettes needs to be conducted for more extensive use of in vitro tests.

Comparative evaluation of the mutagenicity and genotoxicity of smoke condensate derived from Korean cigarettes

OBJECTIVES: Cigarette smoking is associated with carcinogenesis owing to the mutagenic and genotoxic effects of cigarette smoke. The aim of this study was to evaluate the mutagenic and genotoxic effects of Korean cigarettes using in vitro assays. METHODS: We selected 2 types of cigarettes (TL and TW) as benchmark Korean cigarettes for this study, because they represent the greatest level of nicotine and tar contents among Korean cigarettes. Mutagenic potency was expressed as the number of revertants per μg of cigarette smoke condensate (CSC) total particulate matter whereas genotoxic potency was expressed as a concentration-dependent induction factor. The CSC was prepared by the International Organization for Standardization 3308 smoking method. CHO-K1 cells were used in vitro micronucleus (MNvit) and comet assays. Two strains of Salmonella typhimurium (Salmonella enterica subsp.enterica; TA98 and TA1537) were employed in Ames tests. RESULTS: All CSCs showed mutagenicity in the TA98 and TA1537 strains. In addition, DNA damage and micronuclei formation were observed in the comet and MNvit assays owing to CSC exposure. The CSC from the 3R4F Kentucky reference (3R4F) cigarette produced the most severe mutagenic and genotoxic potencies, followed by the CSC from the TL cigarette, whereas the CSC from the TW cigarette produced the least severe mutagenic and genotoxic potencies. CONCLUSIONS: The results of this study suggest that the mutagenic and genotoxic potencies of the TL and TW cigarettes were weaker than those of the 3R4F cigarette. Further study on standardized concepts of toxic equivalents for cigarettes needs to be conducted for more extensive use of in vitro tests.

A Comparison of Intensive Care Unit Mortality Prediction Models through the Use of Data Mining Techniques / 대한의료정보학회지

OBJECTIVES: The intensive care environment generates a wealth of critical care data suited to developing a well-calibrated prediction tool. This study was done to develop an intensive care unit (ICU) mortality prediction model built on University of Kentucky Hospital (UKH)'s data and to assess whether the performance of various data mining techniques, such as the artificial neural network (ANN), support vector machine (SVM) and decision trees (DT), outperform the conventional logistic regression (LR) statistical model. METHODS: The models were built on ICU data collected regarding 38,474 admissions to the UKH between January 1998 and September 2007. The first 24 hours of the ICU admission data were used, including patient demographics, admission information, physiology data, chronic health items, and outcome information. RESULTS: Only 15 study variables were identified as significant for inclusion in the model development. The DT algorithm slightly outperformed (AUC, 0.892) the other data mining techniques, followed by the ANN (AUC, 0.874), and SVM (AUC, 0.876), compared to that of the APACHE III performance (AUC, 0.871). CONCLUSIONS: With fewer variables needed, the machine learning algorithms that we developed were proven to be as good as the conventional APACHE III prediction.