OBJECTIVES: Gender is a major determinant of health behaviors that influences cancer prevention awareness and practices. This study investigated the relationship of the awareness and practice rates of cancer prevention recommendations with gender and socioeconomic status. METHODS: We used data from the Korean National Cancer Prevention Awareness and Practice Survey (2023). The sample included 4,000 men and women aged 20-74 years. We conducted multiple logistic regression analyses to evaluate associations with the awareness and practices of cancer prevention, and a joinpoint regression analysis using age-standardized rates to analyze trends in awareness and practice rates from 2007 to 2023. RESULTS: The awareness rates were 79.4% and 81.2% for men and women, respectively. The overall practice rates were substantially lower (43.1% for men and 48.9% for women). For men, awareness rates did not differ significantly by socio-demographic characteristics, but practice rates increased with age (20-29: 15.9%; 60-74: 53.8%). For women, both awareness (20-29: 73.0%; 60-74: 85.7%) and practice (20-29: 16.8%; 60-74: 67.5%) rates increased with age. The easiest recommendations to follow were “reducing salt intake and avoiding burnt or charred foods” (men: 29.9%; women: 28.4%), whereas the most difficult recommendation was “engaging in regular physical activity” (men: 32.5%; women: 34.4%). CONCLUSIONS While awareness of cancer prevention recommendations was high, the practice of these recommendations was low. Gender influenced changes in awareness and practice rates over time, reflecting a large gap in practice. Future research should explore appropriate intervention points for cancer prevention practices and the development of more effective cancer prevention policies.

Purpose: To estimate the sample size required for developing artificial intelligence (AI) that can predict soft-tissue and alveolar bone changes following orthodontic treatment. Materials and Methods: From the original data sets with N=887, consisting of 132 input and 88 output variables used to create AI models for predicting treatment changes following orthodontic treatment, six subsets of the data (n=75, 150, 300, 450, 600, and 750) were generated through random resampling procedures. The process was repeated four times, resulting in 24 different data subsets. Each data subset was used to create a total of 24 AI models using the TabNet deep neural network algorithm. The clinically acceptable prediction accuracy was defined as a less than 1.5 mm prediction error on the lower lip. The prediction errors from each AI model were compared according to sample sizes and analyzed to estimate the optimal sample size. Results: The prediction error decreased with increasing sample sizes. A training sample size greater than approximately 1650 was estimated to develop an AI model with less than 1.5 mm of prediction errors at the lower lip area. Conclusion From a statistical and research design perspective, a considerable amount of training data appears necessary to develop an AI prediction model with clinically acceptable accuracy.

Purpose: To estimate the sample size required for developing artificial intelligence (AI) that can predict soft-tissue and alveolar bone changes following orthodontic treatment. Materials and Methods: From the original data sets with N=887, consisting of 132 input and 88 output variables used to create AI models for predicting treatment changes following orthodontic treatment, six subsets of the data (n=75, 150, 300, 450, 600, and 750) were generated through random resampling procedures. The process was repeated four times, resulting in 24 different data subsets. Each data subset was used to create a total of 24 AI models using the TabNet deep neural network algorithm. The clinically acceptable prediction accuracy was defined as a less than 1.5 mm prediction error on the lower lip. The prediction errors from each AI model were compared according to sample sizes and analyzed to estimate the optimal sample size. Results: The prediction error decreased with increasing sample sizes. A training sample size greater than approximately 1650 was estimated to develop an AI model with less than 1.5 mm of prediction errors at the lower lip area. Conclusion From a statistical and research design perspective, a considerable amount of training data appears necessary to develop an AI prediction model with clinically acceptable accuracy.

Purpose: To estimate the sample size required for developing artificial intelligence (AI) that can predict soft-tissue and alveolar bone changes following orthodontic treatment. Materials and Methods: From the original data sets with N=887, consisting of 132 input and 88 output variables used to create AI models for predicting treatment changes following orthodontic treatment, six subsets of the data (n=75, 150, 300, 450, 600, and 750) were generated through random resampling procedures. The process was repeated four times, resulting in 24 different data subsets. Each data subset was used to create a total of 24 AI models using the TabNet deep neural network algorithm. The clinically acceptable prediction accuracy was defined as a less than 1.5 mm prediction error on the lower lip. The prediction errors from each AI model were compared according to sample sizes and analyzed to estimate the optimal sample size. Results: The prediction error decreased with increasing sample sizes. A training sample size greater than approximately 1650 was estimated to develop an AI model with less than 1.5 mm of prediction errors at the lower lip area. Conclusion From a statistical and research design perspective, a considerable amount of training data appears necessary to develop an AI prediction model with clinically acceptable accuracy.

OBJECTIVES: Gender is a major determinant of health behaviors that influences cancer prevention awareness and practices. This study investigated the relationship of the awareness and practice rates of cancer prevention recommendations with gender and socioeconomic status. METHODS: We used data from the Korean National Cancer Prevention Awareness and Practice Survey (2023). The sample included 4,000 men and women aged 20-74 years. We conducted multiple logistic regression analyses to evaluate associations with the awareness and practices of cancer prevention, and a joinpoint regression analysis using age-standardized rates to analyze trends in awareness and practice rates from 2007 to 2023. RESULTS: The awareness rates were 79.4% and 81.2% for men and women, respectively. The overall practice rates were substantially lower (43.1% for men and 48.9% for women). For men, awareness rates did not differ significantly by socio-demographic characteristics, but practice rates increased with age (20-29: 15.9%; 60-74: 53.8%). For women, both awareness (20-29: 73.0%; 60-74: 85.7%) and practice (20-29: 16.8%; 60-74: 67.5%) rates increased with age. The easiest recommendations to follow were “reducing salt intake and avoiding burnt or charred foods” (men: 29.9%; women: 28.4%), whereas the most difficult recommendation was “engaging in regular physical activity” (men: 32.5%; women: 34.4%). CONCLUSIONS While awareness of cancer prevention recommendations was high, the practice of these recommendations was low. Gender influenced changes in awareness and practice rates over time, reflecting a large gap in practice. Future research should explore appropriate intervention points for cancer prevention practices and the development of more effective cancer prevention policies.

Purpose: To estimate the sample size required for developing artificial intelligence (AI) that can predict soft-tissue and alveolar bone changes following orthodontic treatment. Materials and Methods: From the original data sets with N=887, consisting of 132 input and 88 output variables used to create AI models for predicting treatment changes following orthodontic treatment, six subsets of the data (n=75, 150, 300, 450, 600, and 750) were generated through random resampling procedures. The process was repeated four times, resulting in 24 different data subsets. Each data subset was used to create a total of 24 AI models using the TabNet deep neural network algorithm. The clinically acceptable prediction accuracy was defined as a less than 1.5 mm prediction error on the lower lip. The prediction errors from each AI model were compared according to sample sizes and analyzed to estimate the optimal sample size. Results: The prediction error decreased with increasing sample sizes. A training sample size greater than approximately 1650 was estimated to develop an AI model with less than 1.5 mm of prediction errors at the lower lip area. Conclusion From a statistical and research design perspective, a considerable amount of training data appears necessary to develop an AI prediction model with clinically acceptable accuracy.

OBJECTIVES: Gender is a major determinant of health behaviors that influences cancer prevention awareness and practices. This study investigated the relationship of the awareness and practice rates of cancer prevention recommendations with gender and socioeconomic status. METHODS: We used data from the Korean National Cancer Prevention Awareness and Practice Survey (2023). The sample included 4,000 men and women aged 20-74 years. We conducted multiple logistic regression analyses to evaluate associations with the awareness and practices of cancer prevention, and a joinpoint regression analysis using age-standardized rates to analyze trends in awareness and practice rates from 2007 to 2023. RESULTS: The awareness rates were 79.4% and 81.2% for men and women, respectively. The overall practice rates were substantially lower (43.1% for men and 48.9% for women). For men, awareness rates did not differ significantly by socio-demographic characteristics, but practice rates increased with age (20-29: 15.9%; 60-74: 53.8%). For women, both awareness (20-29: 73.0%; 60-74: 85.7%) and practice (20-29: 16.8%; 60-74: 67.5%) rates increased with age. The easiest recommendations to follow were “reducing salt intake and avoiding burnt or charred foods” (men: 29.9%; women: 28.4%), whereas the most difficult recommendation was “engaging in regular physical activity” (men: 32.5%; women: 34.4%). CONCLUSIONS While awareness of cancer prevention recommendations was high, the practice of these recommendations was low. Gender influenced changes in awareness and practice rates over time, reflecting a large gap in practice. Future research should explore appropriate intervention points for cancer prevention practices and the development of more effective cancer prevention policies.

OBJECTIVES: Gender is a major determinant of health behaviors that influences cancer prevention awareness and practices. This study investigated the relationship of the awareness and practice rates of cancer prevention recommendations with gender and socioeconomic status. METHODS: We used data from the Korean National Cancer Prevention Awareness and Practice Survey (2023). The sample included 4,000 men and women aged 20-74 years. We conducted multiple logistic regression analyses to evaluate associations with the awareness and practices of cancer prevention, and a joinpoint regression analysis using age-standardized rates to analyze trends in awareness and practice rates from 2007 to 2023. RESULTS: The awareness rates were 79.4% and 81.2% for men and women, respectively. The overall practice rates were substantially lower (43.1% for men and 48.9% for women). For men, awareness rates did not differ significantly by socio-demographic characteristics, but practice rates increased with age (20-29: 15.9%; 60-74: 53.8%). For women, both awareness (20-29: 73.0%; 60-74: 85.7%) and practice (20-29: 16.8%; 60-74: 67.5%) rates increased with age. The easiest recommendations to follow were “reducing salt intake and avoiding burnt or charred foods” (men: 29.9%; women: 28.4%), whereas the most difficult recommendation was “engaging in regular physical activity” (men: 32.5%; women: 34.4%). CONCLUSIONS While awareness of cancer prevention recommendations was high, the practice of these recommendations was low. Gender influenced changes in awareness and practice rates over time, reflecting a large gap in practice. Future research should explore appropriate intervention points for cancer prevention practices and the development of more effective cancer prevention policies.

BACKGROUND/OBJECTIVES: Okra seed is a rich source of various nutritional and bioactive constituents, but its mechanism of action is still unclear. The aim of this study was to evaluated the effects on glucose uptake and serum lipid profiles of unsaponifiable matter (USM) from okra seed in adipocytes and diabetic animal models.MATERIALS/METHODSUSM was prepared from okra seed powder by saponification. The contents of phytosterols and vitamin E in USM were measured. 3T3-L1 preadipocytes were cultured for 6 days with different concentrations of USM (0–200 μg/mL). The diabetic rats were administered with or without USM for 5 wk. RESULTS: In the USM, the contents of phytosterols and vitamin E were 394.13 mg/g USM and 31.16 mg/g USM, respectively. USM showed no cytotoxicity and led to an approximately 1.4-fold increase in glucose uptake in 3T3-L1 adipocytes. The treatment of USM also increased the expressions of peroxisome proliferator-activated receptor-γ and glucose transporter-4 in a dose-dependent manner in adipocytes. The body weight change was not significantly different in all diabetic rats. However, blood glucose and the weights of liver and adipose tissues were significantly reduced compared to those in the control diabetic rats. Treatment with USM decreased the levels of triglycerides, total cholesterol, and low-density lipoprotein cholesterol compared to the control group. The USM group also showed significantly decreased atherogenic indices and cardiac risk factors. CONCLUSION These results suggest that USM from okra seed improves the hypoglycemic and hypolipidemic effects in diabetic rats, and provides valuable information for improving the functional properties of okra seed.

Objectives: This study explored the experiences of medical students enrolled in an elective course titled "Healthcare Innovation and Women's Ventures II" at Ewha Womans University College of Medicine. The research questions were as follows: First, what motivated medical students to participate in the experiential entrepreneurship course? Second, what experiences did the students have during the course? Third, what changes did the students undergo as a result of the course? Methods: Focus group interviews were conducted with six medical students who participated in the experiential entrepreneurship course from February 13 to 23, 2024. Results: The analysis identified three domains, seven categories, and 17 subcategories. In terms of motivations for enrolling in the experiential entrepreneurship course, two categories were identified: "existing interest" and "new exploration." With respect to the experiences gained from the course, three categories emerged: "cognitive experiences," "emotional experiences," and "behavioral experiences." Finally, two categories were identified concerning the changes participants experienced through the course: "changes related to entrepreneurship" and "changes related to career paths." Conclusion Students were motivated to enroll in this course by both their existing interests and their desire to explore new areas. Following the course, they underwent cognitive, emotional, and behavioral changes. Their perceptions of entrepreneurship and career paths were significantly altered.This study is important because it explores the impact of entrepreneurship education in medical schools from the students' perspective.