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.

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

BACKGROUND/AIMS: To define standard reference values for musculoskeletal ultrasonography (MSUS) in Korea. METHODS: A total of 251 healthy adults were recruited for this study. Ultrasonography was performed by experienced rheumatologists who had undergone four appropriate training programs in Korea. A General Electric LOGIQ electronic ultrasound device fitted with a 12 MHz linear transducer was employed. Mean values ± standard deviations (SDs) were defined as standard reference values. Intraclass correlation coefficients was employed to evaluate the extent of inter- and intraobserver agreement when MSUS measurements were made. RESULTS: The 251 study participants included 122 males. Mean subject age was 28.6 years. The average bone-to-capsule distance of the right-side second and third metacarpophalangeal (MCP) joints were 0.68 and 0.72 mm respectively, and those of the left-side joints 0.62 and 0.68 mm. The cartilage thicknesses of the right-side second and third MCP joints were 0.55 and 0.55 mm, and those of the left-side joints were 0.55 and 0.56 mm, respectively. The bone-to-capsule distances of the right and left wrists were 0.80 and 0.82 mm. In 12.4% of participants (31/251), the erosion score of the humeral head was 1.71. In the right-side knee joint, mean cartilage thicknesses of the medial and lateral condyles were 1.86 and 2.03 mm in longitudinal scans. High overall interobserver agreement was evident after appropriate training that included instruction on standard MSUS methodology. CONCLUSIONS We defined standard reference values for MSUS in healthy Korean adults. The reliabilities of interobserver agreements were high after appropriate training program.

PURPOSE: This study analyzed the perception and importance of country-of-origin labeling at restaurants in 500 college students in Jeju surveyed from April 15 to May 5, 2016 with the aim of providing basic data. A total of 465 questionnaires out of 500 were used as base data for this study. METHODS: The data were analyzed using descriptive analysis, χ2-test, and t-test using the SPSS Win program (version 21.0). RESULTS: Regarding food safety-related dietary behaviors, average score was 3.65 points (out of 5), and ‘put the food in a refrigerator or freezer immediately (4.07)’ showed the highest score, whereas ‘cool rapidly hot food prior to putting it in the refrigerator (3.08)’ showed the lowest score. Regarding the awareness of country-of-origin labeling at restaurants, 67.5% of subjects were aware of it. With regard to dietary behavior of food safety, the high group showed a higher score than the low group (p < 0.001). Regarding reliability of the system, 4.9% of subjects indicated ‘very reliable’ and 45.4% ‘somewhat reliable’. For perception of subject's country-of-origin labeling, the average score was 3.77 (out of 5). Regarding checking country-of-origin labeling at restaurants, 68.0% of subjects checked country-of-origin labeling, and the high group in the safety-related dietary behavior score ranking showed a higher rate (79.3%) than the low group (57.1%) (p < 0.001). With regard to importance by item, 'honest country-of-origin labeling of restaurants' showed the highest score at 4.27 (out of 5). CONCLUSION: It is necessary to provide continuing education for college students in order to enhance their perception of country-of-origin labeling at restaurants. Moreover, a systematic and appropriate support and control system by the government and local government needs to be developed in order to improve country-of-origin labeling at restaurants.
Education, Continuing ; Food Safety ; Humans ; Local Government ; Restaurants*