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.

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.

Background: and Purpose This study aimed to evaluate whether extracellular-vesicle-incorporated microRNAs (miRNAs) are potential biomarkers for cancer-related stroke. Methods: This cohort study compared patients with active cancer who had embolic stroke of unknown sources (cancer-stroke group) with patients with only cancer, patients with only stroke, and healthy individuals (control groups). The expression profiles of miRNAs encapsulated in plasma exosomes and microvesicles were evaluated using microarray and validated using quantitative real-time polymerase chain reaction. The XENO-QTM miRNA assay technology was used to determine the absolute copy numbers of individual miRNAs in an external validation cohort. Results: This study recruited 220 patients, of which 45 had cancer-stroke, 76 were healthy controls, 39 were cancer controls, and 60 were stroke controls. Three miRNAs (miR-205-5p, miR-645, and miR-646) were specifically incorporated into microvesicles in patients with cancer-related stroke, cancer controls, and stroke controls. The area under the receiver operating characteristic curves of these three miRNAs were 0.7692–0.8510 for the differentiation of patients with cancer-stroke from cancer-controls and 0.8077–0.8846 for the differentiation of patients with cancer-stroke from stroke controls. The levels of several miRNAs were elevated in the plasma exosomes of patients with cancer, but were lower than those in plasma microvesicles. An in vivo study showed that systemic injection of miR-205-5p promoted the development of arterial thrombosis and elevation of D-dimer levels. Conclusion Stroke due to cancer-related coagulopathy was associated with deregulated expression of miRNAs, particularly microvesicle-incorporated miR-205-5p, miR-645, and miR-646. Further prospective studies of extracellular-vesicle-incorporated miRNAs are required to confirm the diagnostic role of miRNAs in patients with stroke and to screen the roles of miRNAs in patients with cancer.

Objectives: This study aimed to assess hand grip strength and femoral neck bone mineral density levels among Korean fishery workers and investigate their association. Methods: Hand grip strength and femoral neck bone mineral density were measured in a survey and health examination conducted in 2021 among fishery workers in a southern region of South Korea. Covariates including gender, age, education level, income level, smoking behavior, drinking behavior, family history of hip fractures, use of calcium and vitamin D supplements, hypertension, diabetes, regular exercise, and body mass index were investigated. Multiple regression analysis was employed to assess the association between hand grip strength and femoral neck bone mineral density. Results: Among 147 fishery workers, 8.16% exhibited low hand grip strength levels indicative of possible sarcopenia, and a significant association was found between low hand grip strength and decreased femoral neck bone mineral density (β = -89.14, 95% CI = -160.50, -17.78). Additionally, factors such as women gender, advanced age, family history of hip fractures, and a body mass index below 25 kg/m 2 were associated with decreased femoral neck bone mineral density. In the subgroup analysis by gender, a correlation between low hand grip strength and decreased femoral neck bone mineral density was observed only in men. Conclusions Further research is needed to explore various determinants and intervention strategies to prevent musculoskeletal disorders among fishery workers, ultimately enhancing their quality of life and well-being.

Background: As the coronavirus disease 2019 (COVID-19) has continued for a couple of years, the long-term effects of the pandemic and the subsequent school curriculum modification on the mental health of children and parents need to be investigated. To clarify the changes that can occur during one school year and to predict the risk factors for vulnerable groups, this study identified parameters relative to children’s screen time, their problematic behavior, and parental depression. Methods: A total of 186 participants were analyzed who were parents of elementary schoolchildren in South Korea. These parents were required to complete a web-based questionnaire twice. The questionnaires were conducted in June 2020 and September 2021. Participants’ general demographics including family income, children’s screen time, sleep patterns, problematic behavior, and parental depression were assessed via the parental questionnaire that included various measurement tools. Results: Children’s body mass index (BMI) increased significantly in 2021 (18.94 ± 3.75 vs. 18.14 ± 3.30, P < 0.001). Smartphone frequency of use per week (5.35 vs. 4.54, P < 0.001) and screen time per day (3.52 vs. 3.16, P < 0.001) significantly increased during the period of the COVID-19 pandemic. The television screen time (2.88 vs. 3.26, P < 0.001), frequency of viewing (3.77 vs. 4.77, P < 0.001), and children’s problematic behaviors significantly decreased (9.15 vs. 11.85,P < 0.001). A lower income household was a key predictor of increased smartphone frequency (B = 1.840, 95% confidence interval [CI], 0.923–2.757, P < 0.001) and smartphone screen time (B = 1.992, 95% CI, 1.458–2.525, P < 0.001). The results showed that the lower income household (B = 5.624, 95% CI, 2.927–8.320, P < 0.001) and a child’s psychiatric treatment history (B = 7.579, 95% CI, 5.666–9.492, P < 0.001) was the most significant predictor of problematic behaviors of children and parental depression (B = 3.476, 95% CI, 1.628–5.325, P < 0.001; B = 3.138, 95% CI, 1.827–4.450, P < 0.001). Conclusion This study suggested that children’s smartphone screen time and BMI increased during COVID-19 because of the school curriculum modification following school closures in South Korea. The increased children’s problematic behaviors and parental depression were predicted by lower-income households and the previous psychiatric history of children. These results indicate that multiple social support systems to the vulnerable group are needed during the ongoing pandemic and that a modified school setting is required.

Purpose: The purpose of this study was to identify the level of psychological insulin resistance and self-care activities and to evaluate the factors affecting self-care activities in patients with type 2 diabetes mellitus undergoing insulin therapy to provide basic data for the development of educational programs. Methods: Data were collected through the interviews using a structured questionnaire from August 29 to October 20, 2017, from the patients with type 2 diabetes mellitus visiting the Diabetes Mellitus Center at H-General Hospital in J-city. The subjects were 168 patients who had been being treated via self-injection for at least three months after the start of insulin therapy. Data analyses were conducted using t-test, ANOVA, Pearson’s correlation coefficients, and stepwise multiple regression using the SAS WIN 9.2 program. Results: The mean score of psychological insulin resistance was 61.25 (range 19-95) and the mean score of self-care activities was 53.19 (range 18-90). Self-care activities were significantly different by gender (t = -2.94, p = .004), perceived health status (F = 7.00, p < .001), and hypoglycemia during the last three months (t = -2.47, p = .015). Negative correlation was observed between psychological insulin resistance and self-care activities (r = -.33, p < .001). Self-care activities were significantly predicted by psychological insulin resistance, perceived health status, gender, and hypoglycemia during the last three months, and 19.0% of the variance in self-care activities was explained (F = 9.01, p < .001). Conclusion Psychological insulin resistance in patients undergoing insulin therapy and its effects on self-care activities identified in this study will be useful in starting and maintaining insulin therapy in the future.