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: Mycobacterium avium complex (MAC) is a major cause of pulmonary nontuberculous mycobacterial disease; however, treatment outcomes remain suboptimal.Phenotypic drug susceptibility testing (DST) is conditionally recommended; however, conventional broth microdilution is labor-intensive. The Sensititre SLOMYCO® panel offers a standardized platform for DST of slowly growing mycobacteria. Methods: Eighty-six clinical MAC isolates (48 M. avium and 38 M. intracellulare) from respiratory specimens were tested for 13 antimicrobials using the SLOMYCO panel and reference Clinical and Laboratory Standards Institute (CLSI) broth microdilution methods at the Korean Institute of Tuberculosis. Essential agreement (EA) was defined as minimum inhibitory concentrations within ± 1 dilution, and categorical agreement (CA) was based on CLSI 2018 breakpoints for clarithromycin, amikacin, moxifloxacin, and linezolid. Results: The EA was high for amikacin (90%), moxifloxacin (92%), linezolid (92%), and ethambutol (98%). Moderate EA was observed for clarithromycin (79%), ciprofloxacin (67%), and doxycycline (63%), and low EA was observed for trimethoprim-sulfamethoxazole (34%).The CA values were 100%, 77.9%, 69.8%, and 47.7% for clarithromycin, amikacin, moxifloxacin, and linezolid, respectively. All isolates were clarithromycin-susceptible according to both methods, and no clarithromycin- or amikacin-resistant isolates were detected. Conclusion The SLOMYCO DST system demonstrated high agreement with the reference methods for clarithromycin and amikacin in the tested susceptible population. The variability in the results for moxifloxacin and linezolid highlights the need for refined breakpoints. The validation of resistant isolates is essential before the SLOMYCO system can be recommended for comprehensive clinical applications.

Hemorrhagic moyamoya disease (MMD) often manifests with pure intraventricular hemorrhage (IVH) or intracerebral hemorrhage (ICH) with IVH, causing increased intracranial pressure (ICP) and neurological decline. Although intraventricular injection of recombinant tissue plasminogen activator (rt-PA) has been proven safe, its safety in MMD is uncertain. We introduce a case of rescue intraventricular rt-PA injection for an ICP crisis caused by IVH in MMD. A patient with MMD presented with acute ICH with IVH. Bilateral external ventricular drains (EVD) were placed, but the both EVD lost patency immediately due to an intraventricular clot. The next day, due to increased ICP, we injected rt-PA into the right EVD catheter to facilitate intraventricular drainage and reduce clot burden. After injection, IVH volume and ICP decreased significantly without rebleeding, leading to gradual neurological improvement. Intraventricular rt-PA effectively reduced ICP and IVH burden without rebleeding in hemorrhagic MMD, warranting further safety studies.

Background: We aimed to examine the prevalence of burnout among imaging cardiologists in Korea and to identify its associated factors. Methods: An online survey of imaging cardiologists affiliated with university hospitals in Korea was conducted using SurveyMonkey ® in November 2023. The validated Korean version of the Maslach Burnout Inventory-Human Service Survey was used to assess burnout across three dimensions: emotional exhaustion, depersonalization, and lack of personal accomplishment. Data on demographics, work environment factors, and job satisfaction were collected using structured questionnaires. Results: A total of 128 imaging cardiologists (46.1% men; 76.6% aged ≤ 50 years) participated in the survey. Regarding workload, 74.2% of the respondents interpreted over 50 echocardiographic examinations daily, and 53.2% allocated > 5 of 10 working sessions per week to echocardiographic laboratory duties. Burnout levels were high, with a significant proportion of participants experiencing emotional exhaustion (28.1%), depersonalization (63.3%), and a lack of personal accomplishment (92.2%). Younger age (< 50 years) was correlated with higher emotional exhaustion risk, while more research time was protective against burnout in the depersonalization domain. Factors, such as being single, living with family, and specific job satisfaction facets, including uncontrollable workload and value mismatch, were associated with varying levels of burnout risk across different dimensions Conclusion Our study underscores the high burnout rates among Korean imaging cardiologists, attributed to factors such as the subjective environment and job satisfaction.Hence, evaluating and supporting cardiologists in terms of individual values and subjective factors are important to effectively prevent burnout..

Purpose: Vascular endothelial growth factor tyrosine kinase inhibitors (TKIs) have been the standard of care for advanced and metastatic clear cell renal cell carcinoma (ccRCC). However, the therapeutic effect of TKI monotherapy remains unsatisfactory given the high rates of acquired resistance to TKI therapy despite favorable initial tumor response. Materials and Methods: To define the TKI-resistance mechanism and identify new therapeutic target for TKI-resistant ccRCC, an integrative differential gene expression analysis was performed using acquired resistant cohort and a public dataset. Sunitinib-resistant RCC cell lines were established and used to test their malignant behaviors of TKI resistance through in vitro and in vivo studies. Immunohistochemistry was conducted to compare expression between the tumor and normal kidney and verify expression of pathway-related proteins. Results: Integrated differential gene expression analysis revealed increased interferon-induced transmembrane protein 3 (IFITM3) expression in post-TKI samples. IFITM3 expression was increased in ccRCC compared with the normal kidney. TKI-resistant RCC cells showed high expression of IFITM3 compared with TKI-sensitive cells and displayed aggressive biologic features such as higher proliferative ability, clonogenic survival, migration, and invasion while being treated with sunitinib. These aggressive features were suppressed by the inhibition of IFITM3 expression and promoted by IFITM3 overexpression, and these findings were confirmed in a xenograft model. IFITM3-mediated TKI resistance was associated with the activation of TRAF6 and MAPK/AP-1 pathways. Conclusions These results demonstrate IFITM3-mediated activation of the TRAF6/MAPK/AP-1 pathways as a mechanism of acquired TKI resistance, and suggest IFITM3 as a new target for TKI-resistant ccRCC.

A 47-year-old male patient presented with multiple squamous and basal cell carcinomas on the anterior chest, back, and left cheek. The patient experienced odorous discharge from the tumors. Surgical excision was planned, beginning with the anterior chest squamous cell carcinoma. An extensive 32×30 cm cutaneous defect was created, which was covered by a bilateral deep inferior epigastric perforator and pedicled latissimus dorsi myocutaneous flaps. The basal cell carcinomas on the back and squamous cell carcinoma on the left cheek were serially excised, after which the left cheek wound required flap coverage. Postoperative complications such as venous thrombosis and infection led to several reoperations, yet the extensive defect was successfully reconstructed. No local recurrence developed during 31 months of follow-up. We report this case to demonstrate that although the wide excision of very large skin cancers may result in extensive and challenging defects as large as 5.8% of the total body surface area, coverage with appropriate flaps may lead to successful oncologic outcomes and improve the patient’s quality of life.