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: The study aims to confirm the mediating effect of nursing professional pride in the relationship between nursing practice environment, nursing performance, and retention intention. Methods: A descriptive cross-sectional survey was conducted from December 13 to 31, 2021, involving 127 nurses. The following statistical analysis was conducted: t-test, ANOVA, Scheffé test, Pearson's correlation coefficient analysis, and Hayes Process Macro Model 4 (to test the mediating effect). Results: Nursing practice environment showed a significant positive correlation with nursing performance, retention intention, and nursing professional pride. Nursing practice performance showed a positive correlation with retention intention and nursing professional pride, and retention intention showed a significant positive correlation with nursing professional pride. The mediating effect of nursing professional pride was found in the effect of nurses' nursing practice environment on their retention intention. In addition, the mediating effect of nursing professional pride was found in the effect of nurses' nursing practice performance on their retention intention. Conclusion Through this study, it was confirmed that nursing professional pride is a major A factor affecting retention intention in the hospital. Therefore, in order to increase nurses' retention intention to remain in Hospital, the basis of basic data was presented for strategy development.

Purpose: The study aims to confirm the mediating effect of nursing professional pride in the relationship between nursing practice environment, nursing performance, and retention intention. Methods: A descriptive cross-sectional survey was conducted from December 13 to 31, 2021, involving 127 nurses. The following statistical analysis was conducted: t-test, ANOVA, Scheffé test, Pearson's correlation coefficient analysis, and Hayes Process Macro Model 4 (to test the mediating effect). Results: Nursing practice environment showed a significant positive correlation with nursing performance, retention intention, and nursing professional pride. Nursing practice performance showed a positive correlation with retention intention and nursing professional pride, and retention intention showed a significant positive correlation with nursing professional pride. The mediating effect of nursing professional pride was found in the effect of nurses' nursing practice environment on their retention intention. In addition, the mediating effect of nursing professional pride was found in the effect of nurses' nursing practice performance on their retention intention. Conclusion Through this study, it was confirmed that nursing professional pride is a major A factor affecting retention intention in the hospital. Therefore, in order to increase nurses' retention intention to remain in Hospital, the basis of basic data was presented for strategy development.

Purpose: The study aims to confirm the mediating effect of nursing professional pride in the relationship between nursing practice environment, nursing performance, and retention intention. Methods: A descriptive cross-sectional survey was conducted from December 13 to 31, 2021, involving 127 nurses. The following statistical analysis was conducted: t-test, ANOVA, Scheffé test, Pearson's correlation coefficient analysis, and Hayes Process Macro Model 4 (to test the mediating effect). Results: Nursing practice environment showed a significant positive correlation with nursing performance, retention intention, and nursing professional pride. Nursing practice performance showed a positive correlation with retention intention and nursing professional pride, and retention intention showed a significant positive correlation with nursing professional pride. The mediating effect of nursing professional pride was found in the effect of nurses' nursing practice environment on their retention intention. In addition, the mediating effect of nursing professional pride was found in the effect of nurses' nursing practice performance on their retention intention. Conclusion Through this study, it was confirmed that nursing professional pride is a major A factor affecting retention intention in the hospital. Therefore, in order to increase nurses' retention intention to remain in Hospital, the basis of basic data was presented for strategy development.

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: The study aims to confirm the mediating effect of nursing professional pride in the relationship between nursing practice environment, nursing performance, and retention intention. Methods: A descriptive cross-sectional survey was conducted from December 13 to 31, 2021, involving 127 nurses. The following statistical analysis was conducted: t-test, ANOVA, Scheffé test, Pearson's correlation coefficient analysis, and Hayes Process Macro Model 4 (to test the mediating effect). Results: Nursing practice environment showed a significant positive correlation with nursing performance, retention intention, and nursing professional pride. Nursing practice performance showed a positive correlation with retention intention and nursing professional pride, and retention intention showed a significant positive correlation with nursing professional pride. The mediating effect of nursing professional pride was found in the effect of nurses' nursing practice environment on their retention intention. In addition, the mediating effect of nursing professional pride was found in the effect of nurses' nursing practice performance on their retention intention. Conclusion Through this study, it was confirmed that nursing professional pride is a major A factor affecting retention intention in the hospital. Therefore, in order to increase nurses' retention intention to remain in Hospital, the basis of basic data was presented for strategy development.

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.

RESULTS: The administration of ADLE to HFD-induced diabetic mice reduced the hyperplasia, 4-hydroxynonenal levels, and the number of apoptotic cells while improving the insulin levels compared to the HFD group. Treatment of INS-1 cells with palmitate reduced insulin secretion, which was attenuated by the ADLE treatment. Furthermore, the ADLE treatment prevented palmitate-induced cell death in INS-1 cells and isolated islets by reducing the apoptotic signaling molecules, including cleaved caspase-3 and PARP, and the Bax/Bcl2 ratio. ADLE also reduced the levels of reactive oxygen species generation, lipid accumulation, and nitrite production in palmitate-treated INS-1 cells while increasing the ATP levels. This effect corresponded to the decreased expression of inducible nitric oxide synthase (iNOS) mRNA and protein. CONCLUSIONS ADLE helps prevent lipotoxic beta-cell death in INS-1 cells and HFD-diabetic mice, suggesting that ADLE can be used to prevent or treat beta-cell damage in glucose intolerance during the development of diabetes.