PURPOSE: The purpose of this study was to find factors affecting hospital nurses' cancer prevention health promoting behavior METHODS: The subjects were 308 nurses working in general hospitals with over 300 beds. Knowledge of cancer was assessed with 36 question items for six major cancers, and the attitude toward cancer was assessed with 10 items for cancer prevention and early detection. Cancer prevention health promotion behavior was assessed by 21 questions about diet, health life, and exercise. The collected data were analyzed using frequency and percentage, t-tests, one-way ANOVA, Pearson correlation coefficients, and stepwise multiple regression analysis. RESULTS: The score for knowledge of cancer was 25.12±3.33. The average score for attitude towards cancer was 30.41±4.08. The score of cancer prevention health promoting behaviors on cancer was 70.60±10.90. Cancer prevention health promoting behaviors were not correlated with cancer knowledge, and were positively correlated with cancer attitude (r=0.44, p < .001). There was a positive correlation with the cancer prevention attitude (r=0.49, p < .001) among the sub-areas of cancer attitude. CONCLUSION: The nurses' knowledge, attitude, and preventive health promotion behaviors of cancer were important for the health of individuals, patients and the general public, I think it is necessary to search for ways to actively promote cancer prevention health promoting behaviors in various directions and to confirm their effects.
Diet ; Health Promotion ; Hospitals, General* ; Humans

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.

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.

The etiology of disease pathogenesis can be largely explained by genetic variations and several types of environmental factors. In genetically disease-susceptible individuals, subsequent environmental triggers may induce disease development. The human body is colonized by complex commensal microbes that have co-evolved with the host immune system. With the adaptation to modern lifestyles, its composition has changed depending on host genetics, changes in diet, overuse of antibiotics against infection and elimination of natural enemies through the strengthening of sanitation. In particular, commensal microbiota is necessary in the development, induction and function of T cells to maintain host immune homeostasis. Alterations in the compositional diversity and abundance levels of microbiota, known as dysbiosis, can trigger several types of autoimmune and inflammatory diseases through the imbalance of T-cell subpopulations, such as Th1, Th2, Th17 and Treg cells. Recently, emerging evidence has identified that dysbiosis is involved in the progression of rheumatoid arthritis, type 1 and 2 diabetic mellitus, and asthma, together with dysregulated T-cell subpopulations. In this review, we will focus on understanding the complicated microbiota-T-cell axis between homeostatic and pathogenic conditions and elucidate important insights for the development of novel targets for disease therapy.
Anti-Bacterial Agents ; Arthritis, Rheumatoid ; Asthma ; Colon ; Diet ; Dysbiosis ; Genetic Variation ; Genetics ; Homeostasis* ; Human Body ; Immune System ; Life Style ; Microbiota* ; Sanitation ; T-Lymphocytes* ; T-Lymphocytes, Regulatory

Purpose: Epidemiological data on injuries resulting from ice hockey and their management are lacking in Korea. A comprehensive analysis of such data is crucial for the effective prevention and management of ice hockey injuries. This study aimed to determine the epidemiological profile of ice hockey injuries and their management among elite Korean players. Methods: The descriptive epidemiological study involved three semiprofessional male ice hockey teams and used a retrospective self-reported questionnaire for assessment. The data collected included demographic characteristics such as player positions and stick-side preferences, injured body parts, injury types, treatment methods, and the decision-maker for returning to sports. Results: A total of 68 players were included in the study, of whom 58 (85.3%) experienced moderate-to-severe orthopedic injuries. Among the reported injuries, 93 (77.5%) occurred during the games, with player-to-player contact being the most frequent cause of such injuries. The decision to return to sports in 53 cases (44.2%) was made by the medical staff, whereas players and nonmedical staff made that decision in 67 cases (55.8%). The decision-making process of the medical staff for allowing players to return to sports was significantly associated with the player’s position and whether the injury required surgery. Conclusion The study emphasizes the high prevalence of orthopedic injuries among elite ice hockey players in Korea and the importance of injury prevention strategies. It also highlights the need for increased involvement of medical staff in return-to-play decisions to ensure successful recovery of players and their reintegration into the competition.

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.

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: 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.