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.

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.

Objectives: To apply a healthy dietary program with reduced sodium intake, developed using data from the Korea National Health and Nutrition Examination Survey (KNHANES), focusing on the sodium intake level and eating patterns. Methods: The program was implemented using a living lab model, an open innovation ecosystem for user-centered problem-solving. Analysis of the KNHANES data revealed that older age groups had a low energy intake but a high sodium intake, particularly among those who frequently dined out. The program was designed to improve sodium-reduction literacy and enhance practical competency. Over four weeks, 40 participants tracked their dietary intake and worked with a clinical nutritionist through a process of diagnosis, experience, improvement, and expansion. A self-administered survey was conducted before and after the program to assess effectiveness. Results: Participants were four teenagers (10%), 26 in their twenties (65%), and 10 aged ≥ 30 years (25%), with eight males (20%) and 32 females (80%). Post-program analysis showed significant improvements in sodium-related nutrition knowledge (P < 0.01), with increased agreement on adopting low-sodium intake practices (e.g., interest in sodium content, choosing lower-sodium foods). Nutrient intake analysis showed a decrease in energy, carbohydrates, lipids, and proteins (P < 0.001), with sodium intake decreasing from 3,382.37 mg/d to 2,119.05 mg/d (P < 0.001). Conclusion The community-based, living lab model for the sodium-reduction program effectively improved participant sodium-reduction literacy and practical competency, suggesting that step-by-step, autonomous learning, can reduce sodium intake and promote healthier eating habits.

Objectives: This study aimed to develop and apply a dietary program to reduce sugar intake among community residents using a Living Lab approach. Methods: We developed and applied a community-based dietary program to reduce sugar intake. Participants were recruited from community organizations, including a children’s food service management center, elementary to high schools, a university, a family center, a community health center, and an elderly welfare center. The dietary program was conducted in two phases; start and next levels. The start level included a pre-assessment of dietary behaviors and participation in educational platforms, whereas the next level included activities using educational platforms, tailored mission and feedback, and pre- and post-surveys. Extension educators at each community organization implemented the dietary program following organization-specific guidelines. Changes in participants’ nutrition knowledge, dietary behaviors and perceptions, self-efficacy, intention to reduce sugar intake, and participants’ program satisfaction were analyzed using paired t-tests. Results: In total, 1,238 and 339 individuals participated in the start and next level, respectively. Participants reported significantly lower scores on dietary behavior items regarding drinking more juice or soft drinks after program participation (P = 0.009) and craving sweet foods (P = 0.046). They reported a higher intention to take interest in sugar content in food (P = 0.009) and lower-sugar recipes (P = 0.019), eat less food with high sugar content (P = 0.020), and drink water or sparkling water instead of soft drinks (P = 0.042). Nutrition knowledge did not significantly change after program participation. Program satisfaction significantly increased from the start level to the next level (P<0.050). Conclusion This study showed the potential of using a Living Lab approach to implement community-wide dietary interventions. Further research is required to evaluate the effectiveness of the Living Lab approach in various community settings.

Purpose: Evidence on factors influencing the variations of music’s effect on anxiety and pain in surgical patients is unclear. We aimed to elucidate the effects of music intervention on anxiety and pain throughstudy characteristics. Methods: We conducted a search on the PubMed, CINAHL, Embase, Cochrane, and Web of Science databases from March 7 to April 21, 2022, for randomized controlled trials (RCTs) for the effect of music intervention on anxiety, pain, and physiological responses in surgical patients. We included studies published within the last 10 years. We assessed the risk of bias in the study using the Cochrane risk of bias tool for randomized trials and performed meta-analyses using a random-effects model for all outcomes. We used change-from-baseline scores as summary statistics and computed bias-corrected standardized mean differences (Hedges'g) for anxiety and pain outcomes and mean differences (MD) for blood pressure and heart rate. Results: Of the 454 records retrieved, 30 RCTs involving 2280 participants were found to be eligible. Music intervention was found to be superior to standard care in reducing anxiety (Hedges' g = −1.48, 95% confidence interval: −1.97 to −0.98), pain (Hedges's g = −0.67, −1.11 to −0.23), systolic blood pressure (MD = −4.62, −7.38 to −1.86), and heart rate (MD = −3.37, −6.65 to −0.10) in surgical patients. The impact of music on anxiety and pain relief varied significantly depending on the duration of the intervention. The largest effect was observed in interventions lasting between 30 and 60 minutes, with a decrease in anxiety and pain. Conclusions Music intervention is an effective way to reduce anxiety, pain, and physiological responses in surgical patients. Future reviews examining the influence of different types of surgery on the effects of music would add to the body of knowledge in this field. This study has been registered on the International Prospective Register of Systematic Reviews (PROSPERO) under the number CRD42022340203, with a registration date of July 4, 2022.

BACKGROUND/OBJECTIVES: Complementary and alternative medicines can be used to alleviate climacteric symptoms that significantly affect the quality of life of postmenopausal women. Isoflavones are the most common plant-based therapies for postmenopausal changes, but the results of previous studies have been controversial.MATERIALS/METHODS: To investigate whether isoflavones would affect menopausal symptoms as well as ovarian hormones, we performed a systematic review and metaanalysis. The PubMed and EMBASE databases were used to perform the systematic search.Included studies were limited to randomized controlled trials (RCTs) assessing the impact of isoflavone supplementation on menopausal symptoms. RESULTS: Eleven studies were included for the final quantitative assessment. Isoflavone intervention was varied between 49.3 and 135 mg of isoflavones per day for 12 wk–2 yrs.The meta-analysis showed that supplementation of isoflavones significantly increased the estradiol levels (standardized mean difference [SMD] = 0.615, P = 0.035) and Kupperman index (SMD = 3.121, P = 0.003) but had no significant effect on hot flashes, folliclestimulating hormone, and luteinizing hormone. However, both estradiol and the Kupperman index showed significant heterogeneity among studies (I2 = 94.7%, P < 0.001 and I2 = 98.1%, P < 0.001, respectively). CONCLUSIONS Although the results showed a significant SMD in estradiol and the Kupperman index, the results should be interpreted with caution due to the high heterogeneity. Further validation with a larger RCT will be necessary. Overall, isoflavone supplementation has distinct effects on the climacteric symptoms and hormonal changes in postmenopausal women.

In recent years, the taeniasis has been rarely reported in the Republic of Korea (Korea). But in this study, we intend to report 4 taeniasis cases caused by Taenia saginata during a 5-month period (February to June 2018) at a unversity hospital in Gwangju, Korea. Worm samples (proglottids) discharged from all cases were identified by phenotypic and molecular diagnostics. Mitochondrial cytochrome c oxidase subunit I sequences showed 99.4–99.9% identity with T. saginata but, differed by 4% from T. asiatica and by 7% from T. multiceps, respectively. We found that tapeworms in 2 cases (Cases 2 and 3) yielded exactly the same sequences between them, which differed from those in Cases 1 and 4, suggesting intra-species variation in tapeworms. These taeniasis cases by T. saginata infection in this study, which occurred within a limited time period and region, suggest the possibility of a mini-outbreak. This study highlights the need for further epidemiological investigation of potentially overlooked cases of T. saginata infection in Korea.
Cestoda ; Electron Transport Complex IV ; Gwangju ; Korea ; Pathology, Molecular ; Republic of Korea ; Taenia saginata ; Taeniasis