Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Purpose: This study aimed to investigate the influence of perceptions of patient safety culture, job stress, and nursing work environment on patient safety nursing activities among emergency room nurses. Methods: This correlational study was conducted from June 5 to July 31, 2024, and targeted 114 emergency room nurses in Busan. A structured self-report questionnaire was used to collect data. Descriptive statistics, independent sample t-tests, one-way ANOVA, Pearson's correlation coefficients, and multiple regression analyses were employed for data analysis. Results: Patient safety nursing activities significantly differed by age (F=6.17, p=.001) and total clinical experience (F=8.89, p<.001) among the participants' general characteristics. Positive correlations were identified with perceptions of patient safety culture (r=.70, p<.001) and nursing work environment (r=.27, p=.003). Multiple regression analysis indicated that perception of patient safety culture (β=.72, p<.001) and total clinical experience (β=-.32, p=.011) were significant predictors, accounting for 50.5% (F=20.24, p<.001) of the variance. Conclusion The findings indicated that perceptions of patient safety culture and total clinical experience are critical factors to be considered when designing interventions to enhance patient safety nursing activities among emergency room nurses.

Purpose: This study aimed to investigate the influence of perceptions of patient safety culture, job stress, and nursing work environment on patient safety nursing activities among emergency room nurses. Methods: This correlational study was conducted from June 5 to July 31, 2024, and targeted 114 emergency room nurses in Busan. A structured self-report questionnaire was used to collect data. Descriptive statistics, independent sample t-tests, one-way ANOVA, Pearson's correlation coefficients, and multiple regression analyses were employed for data analysis. Results: Patient safety nursing activities significantly differed by age (F=6.17, p=.001) and total clinical experience (F=8.89, p<.001) among the participants' general characteristics. Positive correlations were identified with perceptions of patient safety culture (r=.70, p<.001) and nursing work environment (r=.27, p=.003). Multiple regression analysis indicated that perception of patient safety culture (β=.72, p<.001) and total clinical experience (β=-.32, p=.011) were significant predictors, accounting for 50.5% (F=20.24, p<.001) of the variance. Conclusion The findings indicated that perceptions of patient safety culture and total clinical experience are critical factors to be considered when designing interventions to enhance patient safety nursing activities among emergency room nurses.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Purpose: This study aimed to investigate the influence of perceptions of patient safety culture, job stress, and nursing work environment on patient safety nursing activities among emergency room nurses. Methods: This correlational study was conducted from June 5 to July 31, 2024, and targeted 114 emergency room nurses in Busan. A structured self-report questionnaire was used to collect data. Descriptive statistics, independent sample t-tests, one-way ANOVA, Pearson's correlation coefficients, and multiple regression analyses were employed for data analysis. Results: Patient safety nursing activities significantly differed by age (F=6.17, p=.001) and total clinical experience (F=8.89, p<.001) among the participants' general characteristics. Positive correlations were identified with perceptions of patient safety culture (r=.70, p<.001) and nursing work environment (r=.27, p=.003). Multiple regression analysis indicated that perception of patient safety culture (β=.72, p<.001) and total clinical experience (β=-.32, p=.011) were significant predictors, accounting for 50.5% (F=20.24, p<.001) of the variance. Conclusion The findings indicated that perceptions of patient safety culture and total clinical experience are critical factors to be considered when designing interventions to enhance patient safety nursing activities among emergency room nurses.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Therapeutic efficacy of mesenchymal stem cells (MSCs) is determined by biodistribution and engraftment in vivo.Compared to intravenous infusion, biodistribution of locally transplanted MSCs are partially understood. Here, we performed a pharmacokinetics (PK) study of MSCs after local transplantation. We grafted human MSCs into the brains of immune-compromised nude mice. Then we extracted genomic DNA from brains, lungs, and livers after transplantation over a month. Using quantitative polymerase chain reaction with human Alu-specific primers, we analyzed biodistribution of the transplanted cells. To evaluate the role of residual immune response in the brain, MSCs expressing a cytosine deaminase (MSCs/CD) were used to ablate resident immune cells at the injection site. The majority of the Alu signals mostly remained at the injection site and decreased over a week, finally becoming undetectable after one month. Negligible signals were transiently detected in the lung and liver during the first week. Suppression of Iba1-positive microglia in the vicinity of the injection site using MSCs/CD prolonged the presence of the Alu signals.After local transplantation in xenograft animal models, human MSCs remain predominantly near the injection site for limited time without disseminating to other organs. Transplantation of human MSCs can locally elicit an immune response in immune compromised animals, and suppressing resident immune cells can prolong the presence of transplanted cells. Our study provides valuable insights into the in vivo fate of locally transplanted stem cells and a local delivery is effective to achieve desired dosages for neurological diseases.

Purpose: The purpose of this study was to examine the effects of a mobile application on quality of life, wellness, and preventive behaviors against dementia among older adults who use senior citizen centers. Methods: Seventy-two older adults who used senior citizen centers were allocated to an intervention group (n=36) and a control group (n=36). The experimental treatment involved a cognitive enhancement program for preventing dementia using a mobile application for 12 sessions over 6 weeks. Data were analyzed using the t-test, chi-square test, Fisher’s exact test, paired t-test, and independent t-test. Results: The cognitive enhancement program based on a mobile application was effective for wellness (t = -3.87, p < .001) and preventive behaviors against dementia (t = -3.98, p < .001) for older adults who used a senior center. Conclusion The mobile application-based cognitive enhancement program developed in this study is recommended as an effective intervention for dementia prevention in older adults.