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.

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.

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.

Purpose: The purpose of this study was to explore the experiences of healthcare disparities in the individuals with acquired vision impairments. Methods: This study is a qualitative research using thematic analysis. Data were collected from January to March 2024 through one-on-one semi-structured interviews with a total of 11 individuals with acquired vision impairments. Results: The analysis revealed 5 main themes and 19 subthemes. The identified main themes were physical injury and aggravation, psychological tension, difficulty maintaining a healthy lifestyle, mastery of self-management and emergence of social requirements. Conclusion The findings of this study contribute to a deep understanding of the health management experiences of individuals with acquired vision impairments. Additionally, this study identifies their healthcare needs and provides directions for rehabilitation nursing and health promotion behaviors. It is necessary to explore methods for developing tailored health care programs for individuals with acquired vision impairments and to address their needs for physical environments and social systems.

Purpose: The study aimed to investigate the recruiting of T lymphocytes including IL-7Rαlow CX3CR1+ effector memory (EM) CD8+ T cells and α4β7 integrin tagged T cells to inflamed intestinal mucosa. Methods: Whole blood and mucosal tissues of intestine were collected from 40 children with or without inflammatory bowel disease (IBD). T cell surface staining and immunohistochemistry were done with several antibodies in peripheral blood mononuclear cells (PBMCs) and intestinal mucosa, respectively. Serum levels of cytokines were measured by ELISA. Results: The frequency of IL-7Rαlow CX3CR1+ EM CD8+ T cells in the PBMC was significantly higher in the ulcerative colitis group than in the control group (57.9±17.80% vs. 33.9±15.70%, p=0.021). The frequency of integrin α4β7+ CD4+ T cells in the PBMC was significantly lower in the ulcerative colitis group than in the control group (53.2±27.6% vs. 63.9±13.2%, p=0.022). Serum concentration of TNF-α was higher in the Crohn’s disease group than in the control group (26.13±5.01 pg/mL vs. 19.65±6.07 pg/mL, p=0.008). Of the three groups, the ulcerative colitis group had the highest frequency of integrin α4β7+ T cells based on immunohistochemistry analyses for intestinal tissues, followed by the Crohn’s disease group and the control group (4.63±1.29 cells vs. 2.0±0.57 cells vs. 0.84±0.52 cells, p<0.001). Conclusion Trafficking immune cells with effector memory CD8+ T cells clarified by IL-7Rαlow CX3CR1+ and integrin α4β7+ CD4+ T cells might be highly associated with the pathogenesis of ulcerative colitis.