Herein, we report a rare case of Bowen’s disease in the umbilicus. A 42-year-old woman visited our clinic with a brownish-to-blackish plaque on the umbilicus, which was about 0.8 cm in diameter and neither itchy nor painful.Histopathological examination of the lesion revealed Bowen’s disease.

PURPOSE: The purpose of this study was to examine the association between health behaviors and health-related quality of life (HRQOL) among vulnerable children in a community. METHODS: Using data from 'The Obesity Prevention Framework for Vulnerable Children', a secondary analysis was conducted for 165 children (ages 8~12 years) and their parents who were recruited from 16 K-gu Community Child Centers in Seoul. Six types of health behaviors related to eating and activity were assessed. Each behavior was categorized into the non-recommended vs. recommended levels. The scores of the recommended levels of the six health behaviors were summed up for the composite score of health behaviors. HRQOL was measured by KIDSCREEN-52. RESULTS: The groups with a non-recommended level of fast food intake and sedentary behavior had a significantly lower total score of KIDSCREEN-52 than those with a recommended level. Moreover, the lower composite score of health behaviors was significantly associated with the lower total score of KIDSCREEN-52. CONCLUSION: Among the vulnerable children, the six recommended health behaviors and their composite score were in significant positive associations with the HRQOL levels. Therefore, nursing strategies for enhancing the recommended levels of health behaviors are needed for vulnerable children.
Child* ; Eating ; Fast Foods ; Health Behavior* ; Humans ; Nursing ; Obesity ; Parents ; Quality of Life* ; Seoul ; Vulnerable Populations ; Child Health

Purpose: This study aimed to valuate the reliability and validity of the Korean version of the 5C Psychological Antecedents of Vaccination (K-5C) scale. Methods: The English version of the 5C scale was translated into Korean, following the World Health Organization guidelines. Data were collected from 316 community-dwelling adults. Content validity was evaluated using the content validity index, while construct validity was evaluated through confirmatory factor analysis. Convergent validity was examined by assessing the correlation with vaccination attitude, and concurrent validity was evaluated by examining the association with coronavirus disease 2019 (COVID-19) vaccination status. Internal consistency and test-retest reliability were also evaluated. Results: Content validity results indicated an item-level content validity index ranging from .83 to 1, and scale-level content validity index, averaging method was .95. Confirmatory factor analysis supported the fit of the measurement model, comprising a five-factor structure with a 15-item questionnaire (RMSEA = .05, SRMR = .05, CFI = .97, TLI = .96). Convergent validity was acceptable with a significant correlation between each sub-scale of the 5C scale and vaccination attitude. In concurrent validity evaluation, confidence, constraints, and collective responsibility of the 5C scale were significant independent predictors of the current COVID-19 vaccination status. Cronbach’s alpha for each subscale ranged from .78 to .88, and the intraclass correlation coefficient for each subscale ranged from .67 to .89. Conclusion The Korean version of the 5C scale is a valid and reliable tool to assess the psychological antecedents of vaccination among Korean adults.

Owing to donor-related issues, blood shortages and transfusion-related adverse reactions have become global issues of grave concern. In vitro manufactured red blood cells (RBCs) are promising substitutes for blood donation. In the United Kingdom, a clinical trial for allogeneic mini transfusion of cultured RBCs derived from primary hematopoietic stem cells has recently begun. However, current production quantities are limited and need improved before clinical use. New methods to enhance manufacturing efficiencies have been explored, including different cell sources, bioreactors, and 3-dimensional (3D) materials; however, further research is required. In this review, we discuss various cell sources for blood cell production, recent advances in bioreactor manufacturing processes, and the clinical applications of cultured blood.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

Purpose: In this study, we examined change in the number of cataract surgeries from 2013 to 2018, since the implementation of institutional changes in 2012, and the introduction of diagnosis-related groups (DRGs) and a gradual reduction in selective-medical expenses from 2014. Methods: Based on data from the main surgery statistical yearbook provided by the Korea National Health Insurance Service (KNHIS), we extracted the number of cataract surgeries nationwide by year from 2013 to 2018. Data were divided by sex, age, regions, and level of healthcare providers in an effort to understand changes that occurred in the number of cataract surgeries and the reasons for these changes. Statistical analysis was carried out using joint point regression. Results: The total number of cataract surgeries per 100,000 people increased by 32.9% over the six-year period, with an annual average increase of 5.9%. Females (58.0-59.2%) had more cataract surgeries than males (40.8-42.0%). Additionally, the number of cataract surgeries per 100,000 people rose over the six-year time frame for those aged under 40 years, and for those in their 40s, 50s, and 60s. In terms of regions and patients’ residence, urban areas such as Seoul, Pusan, and Daegu showed an increase in surgeries performed; most provinces, however, with the exception of Jeju Island, indicated a relative decline in cataract surgeries. There was no difference in the number of cataract surgeries performed over the six-year period in terms of the level of healthcare providers. Conclusions The number of cataract surgeries per 100,000 people rose over the six-year period between 2013 and 2018. By region, an increasing trend was observed in urban areas; however, the level of the healthcare providers did not appear to have an effect on the number of cataract surgeries performed.