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: Data on drug-coated balloon (DCB) treatment in elderly patients are limited. This study was to evaluate the efficacy of DCB treatment in percutaneous coronary intervention (PCI) among elderly patients. METHODS: A retrospective analysis included 232 patients aged 75 years or older with coronary artery disease who underwent successful PCI using either DCB alone or in combination with drug-eluting stent (DES) based on pre-dilation results (DCB-based PCI). These patients were compared with 1818 elderly patients who underwent second-generation DES implantation (DES-only PCI). The endpoint was major adverse cardiovascular events (MACE) at 2-year follow-up. RESULTS: In the DCB-based PCI, 61.2% of patients received DCB-only treatment. Compared to DES-only PCI, the DCB-based PCI group had fewer stents (0.5 ± 0.7 and 1.7 ± 0.8, P < 0.001), shorter stent lengths (13.3 ± 20.9 mm and 37.4 ± 23.0 mm, P < 0.001), and lower usage of small stents with a diameter of 2.5 mm or less (15.6% and 28.7%, P = 0.010). The DCB-based PCI group exhibited lower rate of MACE (5.5% and 13.1%, P = 0.003), target vessel revascularization (1.1% and 5.6%, P = 0.017) and major bleeding (0.7% and 5.1%, P = 0.009) at 2-year follow-up. The reduced risk in 2-year MACE was consistently observed across various matching procedures, with the most significant reduction noted in target vessel revascularization and major bleeding. CONCLUSION The DCB-based PCI reduced stent burden, particularly in the usage of small diameter stents, and was associated with lower risks of MACE, target vessel revascularization, and major bleeding compared to DES-only PCI in elderly patients.

Purpose: This subgroup analysis of the Korean subset of patients in the phase 3 LASER301 trial evaluated the efficacy and safety of lazertinib versus gefitinib as first-line therapy for epidermal growth factor receptor mutated (EGFRm) non–small cell lung cancer (NSCLC). Materials and Methods: Patients with locally advanced or metastatic EGFRm NSCLC were randomized 1:1 to lazertinib (240 mg/day) or gefitinib (250 mg/day). The primary endpoint was investigator-assessed progression-free survival (PFS). Results: In total, 172 Korean patients were enrolled (lazertinib, n=87; gefitinib, n=85). Baseline characteristics were balanced between the treatment groups. One-third of patients had brain metastases (BM) at baseline. Median PFS was 20.8 months (95% confidence interval [CI], 16.7 to 26.1) for lazertinib and 9.6 months (95% CI, 8.2 to 12.3) for gefitinib (hazard ratio [HR], 0.41; 95% CI, 0.28 to 0.60). This was supported by PFS analysis based on blinded independent central review. Significant PFS benefit with lazertinib was consistently observed across predefined subgroups, including patients with BM (HR, 0.28; 95% CI, 0.15 to 0.53) and those with L858R mutations (HR, 0.36; 95% CI, 0.20 to 0.63). Lazertinib safety data were consistent with its previously reported safety profile. Common adverse events (AEs) in both groups included rash, pruritus, and diarrhoea. Numerically fewer severe AEs and severe treatment–related AEs occurred with lazertinib than gefitinib. Conclusion Consistent with results for the overall LASER301 population, this analysis showed significant PFS benefit with lazertinib versus gefitinib with comparable safety in Korean patients with untreated EGFRm NSCLC, supporting lazertinib as a new potential treatment option for this patient population.

Objective: This study aimed to investigate the closure rate and adverse effects of combination therapy with acetaminophen and ibuprofen for hemodynamically significant patent ductus arteriosus (hsPDA) compared with monotherapy with ibuprofen in extremely preterm infants (EPTs). Methods: This was a single-center, retrospective, and historical control study of infants with hsPDA born at <28 weeks of gestation and a birth weight <1,000 g. Based on the first-line therapeutic policy for hsPDA, the cohort was classified into a monotherapy group (period I: January 2019-July 2021) and a combination therapy group (period II: September 2021-August 2023). Baseline characteristics, treatment outcomes, adverse effects, and morbidities were compared between the groups. Results: Of the 43 EPTs with hsPDA, 26 received monotherapy with ibuprofen during period I, and 17 received combination therapy with acetaminophen and ibuprofen during period II. The successful closure rates after the first medical therapy were 42.3% in the monotherapy group vs. 76.5% in the combination therapy group (P=0.027). No significant difference in adverse effects during medication use was observed between the groups. Conclusion Combination therapy with acetaminophen and ibuprofen improved the closure rate for hsPDA without detectable adverse effects. Combination therapy could be considered the first therapeutic option for hsPDA in EPTs. Further well-designed studies are needed to define the safety and effectiveness of combination therapy.

Objective: This study aimed to investigate the closure rate and adverse effects of combination therapy with acetaminophen and ibuprofen for hemodynamically significant patent ductus arteriosus (hsPDA) compared with monotherapy with ibuprofen in extremely preterm infants (EPTs). Methods: This was a single-center, retrospective, and historical control study of infants with hsPDA born at <28 weeks of gestation and a birth weight <1,000 g. Based on the first-line therapeutic policy for hsPDA, the cohort was classified into a monotherapy group (period I: January 2019-July 2021) and a combination therapy group (period II: September 2021-August 2023). Baseline characteristics, treatment outcomes, adverse effects, and morbidities were compared between the groups. Results: Of the 43 EPTs with hsPDA, 26 received monotherapy with ibuprofen during period I, and 17 received combination therapy with acetaminophen and ibuprofen during period II. The successful closure rates after the first medical therapy were 42.3% in the monotherapy group vs. 76.5% in the combination therapy group (P=0.027). No significant difference in adverse effects during medication use was observed between the groups. Conclusion Combination therapy with acetaminophen and ibuprofen improved the closure rate for hsPDA without detectable adverse effects. Combination therapy could be considered the first therapeutic option for hsPDA in EPTs. Further well-designed studies are needed to define the safety and effectiveness of combination therapy.

Central odontogenic fibroma (COF) is a rare tumor, accounting for only 0.1% of all odontogenic tumors of the jaw. Clinically, these tumors grow slowly and expand the cortical bone without causing pain. Radiographically, they typically appear as unilocular radiolucent lesions with relatively well-defined linings, although multilocular lesions can also be observed. In some cases, the lesion may lead to root resorption of affected teeth and increased tooth mobility. The standard treatment for COF is surgical excision.However, due to its rarity, the optimal approach regarding affected tooth extraction remains unclear. In this report, we present cases of COF in 58- and 56-year-old females, outlining the diagnostic workup, treatment strategy, and postoperative outcomes, particularly regarding affected tooth extraction. Through this case study, we aim to contribute to the existing literature on COF management and achieve successful treatment outcomes.