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: 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.

After anaplastic large-cell lymphoma (ALCL) was first described by Stain in 1985, there have been several histological variants of ALCL reported. There are classified histological subtypes of ALCL, such as lymphohistiocytic, small cell, Hodgkin-like, composite pattern, and other less common variants including neutrophil-rich ALCL. A 63-year-old male patient presented with erythematous exophytic mass on the left lower leg. In the past, his condition had been diagnosed as abdominal primary cutaneous ALCL (pcALCL), which recurred as systemic ALCL (sALCL) in the left bronchus. After treatment, he achieved complete remission. Histopathologic examination showed large-sized pleomorphic, anaplastic mitotic tumor cells, several neutrophils, and a few lymphocytes. Neutrophil-rich ALCL is a rare histological variant of ALCL. It is characterized by the presence of CD30-positive anaplastic tumor cells with numerous neutrophil infiltrations. Neutrophil-rich ALCL responds well to treatment but tends to recur. There were four cases reported to have recurrent neutrophilrich ALCL. All cases were diagnosed with neutrophil-rich pcALCL prior to recurrence.Three cases had local recurrence, and only one case relapsed as sALCL. Herein, we present the first case of neutrophil-rich ALCL recurring as sALCL twice.

We assessed the risk factors for major amputation of diabetic foot ulcers (DFUs) in patients with diabetic kidney disease (DKD) stages 3b–5. For DFU assessment, in addition to DFU location and presence of infection, ischemia, and neuropathy, vascular calcification was assessed using the medial arterial calcification (MAC) score. Of 210 patients, 26 (12.4%) underwent major amputations. Only the location and extension of DFU, represented by Texas grade differed between the minor and major amputation groups. However, after adjusting for covariates, ulcer location of mid- or hindfoot (vs. forefoot, odds ratio [OR] = 3.27), Texas grades 2 or 3 (vs. grade 0, OR = 5.78), and severe MAC (vs. no MAC, OR = 4.46) was an independent risk factor for major amputation (all P < 0.05). The current use of antiplatelets was a possible protective factor for major amputations (OR = 0.37, P = 0.055). In conclusion, DFU with severe MAC is associated with major amputation in patients with DKD.

Background: This study aimed to determine the effect of ramen sauce on tooth tone changes over time, after selecting three different ramen colors from the ramens sold in the market, and applying the sauce to bovine teeth. Methods: Healthy bovine teeth were selected, and cutting discs were used to produce 60 specimens (5×5×3 mm), with 15 specimens distributed per county. Three types of ramen (buldak, chacharoni black bean sauce, and ottogi curry noodle) were used as the experimental group, and water was used as the negative control group. Tooth tone measurement was performed using a spectrophotometer (CM-700d) to measure the color before and after 1 (3 h 44 min), 2 (7 h 28 min), 3 (11 h 12 min), and 4 weeks (14 h 56 min). Analysis of the color tone change was performed using Statistical Package for the Social Sciences version 28. Results: In the experimental group, there was a significant color tone change before and after immersion. L* indicated the largest change in black bean sauce ramen, a* indicated buldak ramen, and b* indicated the largest change in curry ramen. The amount of color change (ΔE*) was the largest in curry ramen, followed by buldak and black bean sauce ramens. The results of the post-hoc analysis showed significant differences between all groups except buldak and black bean sauce ramens. Conclusion All three types of ramen revealed significant color change before and after immersion, and curry ramen showed the largest amount of color change among them.

Purpose: This study investigated the impact of nurse practice environment and organizational justice on nurses’job satisfaction. Methods: We identified the factors between nursing work environment and organizational justice to job satisfaction for 189 nurses working at a general hospital in city C. Data were collected from June 1st to 15th, 2023, and analyzed using descriptive statistics, t-test, ANOVA, Pearson’s correlation coefficients, and multiple stepwise regression, using IBM/SPSS 27.0 for the Windows program. Results: The mean job satisfaction was 3.24±0.55 points on a 5-point scale. Multiple stepwise regression revealed that the factors affecting nurses’job satisfaction included nursing foundations for quality of care (β=.26, p=.005), staffing and resource adequacy (β=.40, p<.001), collegial nurse-physician relations (β=-.24, p=.007), and distributive justice(β =.27, p<.001).These variables explained 55.0% of job satisfaction. Conclusion The research findings indicate that higher job satisfaction is associated with a better nurse practice environment and positive perceptions of organization justice. These findings indicate that it is necessary to enhance the nurse practice environment and improve organizational justice to enhance job satisfaction among nurses.