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.

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

OBJECTIVES: Diabetes and alcohol dependence are considered as independent risk factors for cognitive impairment. This research was to investigate whether cognitive functions in diabetic alcohol dependent patients were more impaired than non-diabetic alcohol dependent patients. METHODS: A cross-sectional study was conducted in alcohol dependence patients (n=138). Patients with alcohol dependence diagnosed by Diagnostic and Statistical Manual of Mental Disorder, 4th edition, Text Revision underwent a 75 g oral glucose tolerance test, to classify to diabetics group and non-diabetics group. In addition to demographic and clinical characteristics, cognitive functions assessed using the Korean-Mini Mental Status Examination (K-MMSE), word list memory test, and word fluency test, word list recall test from Korean version of the consortium to establish a registry for Alzheimer's disease, and block design test, digit span test, and digit symbol test from Korean-Wechsler Adult Intellogence Scale were compared between the two groups. RESULTS: There was no significant difference in demographic and other clinical characteristics between the non-diabetic and diabetic alcoholic patients. Compared to non-diabetic alcoholic patients, diabetic alcoholic patients were more impaired on language of K-MMSE (p=0.028) and digit symbol test (p=0.044). CONCLUSION: These findings suggest the more severe impairment of selective cognitive functions in diabetic alcoholic patients than non-diabetic alcoholic patients. Future replication of these findings in a large population is necessary.
Adult ; Alcoholics ; Alcoholism ; Alzheimer Disease ; Cognition ; Comorbidity ; Cross-Sectional Studies ; Diabetes Mellitus ; Glucose Tolerance Test ; Humans ; Memory ; Mental Disorders ; Risk Factors

Toxoplasma gondii Korean isolate (KI-1) tachyzoites were inoculated intraduodenally to BALB/c mice using a silicon tube, and the course of infection and immune responses of mice were studied. Whereas control mice, that were infected intraperitoneally, died within day 7 post-infection (PI), the intraduodenally infected mice survived until day 9 PI (infection with 1x10(5) tachyzoites) or day 11 PI (with 1x10(6) tachyzoites). Based on histopathologic (Giemsa stain) and PCR (B1 gene) studies, it was suggested that tachyzoites, after entering the small intestine, invaded into endothelial cells, divided there, and propagated to other organs. PCR appeared to be more sensitive than histopathology to detect infected organs and tissues. The organisms spread over multiple organs by day 6 PI. However, proliferative responses of splenocytes and mesenteric lymph node (MLN) cells in response to con A or Toxoplasma lysate antigen decreased significantly, suggesting immunosuppression. Splenic CD4+ and CD8+ T-lymphocytes showed decreases in number until day 9 PI, whereas IFN-gamma and IL-10 decreased slightly at day 6 PI and returned to normal levels by day 9 PI. No TNF-alpha was detected throughout the experimental period. The results showed that intraduodenal infection with KI-1 tachyzoites was successful but did not elicit significant mucosal immunity in mice and allowed dissemination of T. gondii organisms to systemic organs. The immunosuppression of mice included reduced lymphoproliferative responses to splenocytes and MLN cells to mitogen and low production of cytokines, such as IFN-gamma, TNF-alpha, and IL-10, in response to T. gondii infection.
Animals ; Cell Proliferation ; Cytokines/secretion ; Disease Models, Animal ; Duodenum/immunology/parasitology/pathology ; Endothelial Cells/parasitology ; Histocytochemistry ; Immune Tolerance ; Lymph Nodes/immunology ; Mice ; Mice, Inbred BALB C ; Polymerase Chain Reaction ; Rodent Diseases/immunology/parasitology/pathology ; T-Lymphocyte Subsets/immunology ; Toxoplasma/*immunology/pathogenicity ; Toxoplasmosis, Animal/*immunology/parasitology/pathology

OBJECTIVE: Our aim was to evaluate the changes in blood glucose control and lipid profiles after 2-months of smoking cessation in healthy males. METHODS: Smoking abstinence was evaluated through self-report and urine cotinine levels. 12 individuals who succeeded in quitting smoking were analyzed. Fasting values of glucose and insulin were used to estimate the beta-cell activity and insulin resistance was evaluated using the Homeostasis Model Assessment (HOMA) and Quantitative Insulin Sensitivity Check Index (QUICKI). RESULTS: The data showed that the subjects had a significant increase in weight, body mass index and fasting plasma glucose levels after smoking cessation. The HOMA-Insulin Resistance and the HOMA beta-cell function increased significantly (p=0.005, p=0.047 respectively). The QUICKI showed a significant decrease (p=0.005). In addition, the low-density lipoprotein cholesterol levels decreased significantly (p=0.028); however, changes in the high-density lipoprotein cholesterol, the triglyceride and total cholesterol levels were not significant (p=0.284, p=0.445 respectively). CONCLUSION: During the initial stage of smoking abstinence, insulin resistance increased and insulin sensitivity decreased due to elevated body weight and fat composition. Therefore, it is important to educate individuals that stop smoking about the necessity of weight control during smoking cessation programs.
Blood Glucose ; Body Weight ; Cholesterol ; Cotinine ; Fasting ; Glucose ; Homeostasis ; Humans ; Insulin ; Insulin Resistance ; Lipoproteins ; Male ; Plasma ; Smoke ; Smoking ; Smoking Cessation