Purpose: 11C-Methionine PET/CT is a promising method for detecting parathyroid lesions in patients with primary hyperparathyroidism (PHPT). We aimed to determine the diagnostic ability and correlation of digital 11C-Methionine PET/CT for parathyroid lesions in patients with PHPT, particularly in cases where standard imaging methods yielded inconclusive results. Methods: This retrospective analysis was conducted on patients diagnosed with PHPT who underwent digital 11C-Methionine PET/CT imaging because of ambiguous results on standard imaging work-up ( 99m Tc-MIBI parathyroid scan and/or neck ultrasonography). Quantitative 11C-Methionine PET/CT parameters, including maximum standardized uptake value (SUVmax), mean SUV (SUVmean), peak SUV (SUVpeak), parathyroid methionine volume (PMV), and whole methionine uptake(WMU: PMV multiplied by SUVmean) were calculated with various thresholds, and their correlations with biochemical andpathologic parameters were investigated. Results: This study included 22 consecutive patients (10 men and 12 women) with a median age of 64.0 years. The lesion detection rate and sensitivity of digital 11C-Methionine PET/CT were 81.8% (18/22) and 100.0% (18/18), respectively.Quantitative analysis revealed that serum PTH (r = 0.490, P = 0.039) and serum calcium (r = 0.583, P = 0.011) were signifi-cantly correlated with PMV50%. Conclusion Digital 11C-Methionine PET/CT offers good performance in the detection of parathyroid lesions in PHPT patients with inconclusive standard imaging work-up. The volume parameter of PMV50% significantly correlated biochemi-cal parameters and can serve as a complementary diagnostic tool.

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.

This study explores the development, roles, and key initiatives of the Regional Environmental Health Centers in Korea, detailing their evolution through four distinct phases and their impact on environmental health policy and local governance. It chronicles the establishment and transformation of these centers from their inception in May 2007, through four developmental stages. Originally named Environmental Disease Research Centers, they were subsequently renamed Environmental Health Centers following legislative changes. The analysis includes the expansion in the number of centers, the transfer of responsibilities to local governments, and the launch of significant projects such as the Korean Children’s Environmental Health Study (Ko-CHENS ). During the initial phase (May 2007–February 2009), the 10 centers concentrated on research-driven activities, shifting from a media-centered to a receptor-centered approach. In the second phase, prompted by the enactment of the Environmental Health Act, six additional centers were established, broadening their scope to address national environmental health issues. The third phase introduced Ko-CHENS, a 20-year national cohort project designed to influence environmental health policy by integrating research findings into policy frameworks. The fourth phase marked a decentralization of authority, empowering local governments and redefining the centers' roles to focus on regional environmental health challenges. The Regional Environmental Health Centers have significantly evolved and now play a crucial role in addressing local environmental health issues and supporting local government policies. Their capacity to adapt and respond to region-specific challenges is essential for the effective implementation of environmental health policies, reflecting geographical, socioeconomic, and demographic differences.

Purpose: 11C-Methionine PET/CT is a promising method for detecting parathyroid lesions in patients with primary hyperparathyroidism (PHPT). We aimed to determine the diagnostic ability and correlation of digital 11C-Methionine PET/CT for parathyroid lesions in patients with PHPT, particularly in cases where standard imaging methods yielded inconclusive results. Methods: This retrospective analysis was conducted on patients diagnosed with PHPT who underwent digital 11C-Methionine PET/CT imaging because of ambiguous results on standard imaging work-up ( 99m Tc-MIBI parathyroid scan and/or neck ultrasonography). Quantitative 11C-Methionine PET/CT parameters, including maximum standardized uptake value (SUVmax), mean SUV (SUVmean), peak SUV (SUVpeak), parathyroid methionine volume (PMV), and whole methionine uptake(WMU: PMV multiplied by SUVmean) were calculated with various thresholds, and their correlations with biochemical andpathologic parameters were investigated. Results: This study included 22 consecutive patients (10 men and 12 women) with a median age of 64.0 years. The lesion detection rate and sensitivity of digital 11C-Methionine PET/CT were 81.8% (18/22) and 100.0% (18/18), respectively.Quantitative analysis revealed that serum PTH (r = 0.490, P = 0.039) and serum calcium (r = 0.583, P = 0.011) were signifi-cantly correlated with PMV50%. Conclusion Digital 11C-Methionine PET/CT offers good performance in the detection of parathyroid lesions in PHPT patients with inconclusive standard imaging work-up. The volume parameter of PMV50% significantly correlated biochemi-cal parameters and can serve as a complementary diagnostic tool.

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.

This study explores the development, roles, and key initiatives of the Regional Environmental Health Centers in Korea, detailing their evolution through four distinct phases and their impact on environmental health policy and local governance. It chronicles the establishment and transformation of these centers from their inception in May 2007, through four developmental stages. Originally named Environmental Disease Research Centers, they were subsequently renamed Environmental Health Centers following legislative changes. The analysis includes the expansion in the number of centers, the transfer of responsibilities to local governments, and the launch of significant projects such as the Korean Children’s Environmental Health Study (Ko-CHENS ). During the initial phase (May 2007–February 2009), the 10 centers concentrated on research-driven activities, shifting from a media-centered to a receptor-centered approach. In the second phase, prompted by the enactment of the Environmental Health Act, six additional centers were established, broadening their scope to address national environmental health issues. The third phase introduced Ko-CHENS, a 20-year national cohort project designed to influence environmental health policy by integrating research findings into policy frameworks. The fourth phase marked a decentralization of authority, empowering local governments and redefining the centers' roles to focus on regional environmental health challenges. The Regional Environmental Health Centers have significantly evolved and now play a crucial role in addressing local environmental health issues and supporting local government policies. Their capacity to adapt and respond to region-specific challenges is essential for the effective implementation of environmental health policies, reflecting geographical, socioeconomic, and demographic differences.

Purpose: The study aims to confirm the mediating effect of nursing professional pride in the relationship between nursing practice environment, nursing performance, and retention intention. Methods: A descriptive cross-sectional survey was conducted from December 13 to 31, 2021, involving 127 nurses. The following statistical analysis was conducted: t-test, ANOVA, Scheffé test, Pearson's correlation coefficient analysis, and Hayes Process Macro Model 4 (to test the mediating effect). Results: Nursing practice environment showed a significant positive correlation with nursing performance, retention intention, and nursing professional pride. Nursing practice performance showed a positive correlation with retention intention and nursing professional pride, and retention intention showed a significant positive correlation with nursing professional pride. The mediating effect of nursing professional pride was found in the effect of nurses' nursing practice environment on their retention intention. In addition, the mediating effect of nursing professional pride was found in the effect of nurses' nursing practice performance on their retention intention. Conclusion Through this study, it was confirmed that nursing professional pride is a major A factor affecting retention intention in the hospital. Therefore, in order to increase nurses' retention intention to remain in Hospital, the basis of basic data was presented for strategy development.

Purpose: The study aims to confirm the mediating effect of nursing professional pride in the relationship between nursing practice environment, nursing performance, and retention intention. Methods: A descriptive cross-sectional survey was conducted from December 13 to 31, 2021, involving 127 nurses. The following statistical analysis was conducted: t-test, ANOVA, Scheffé test, Pearson's correlation coefficient analysis, and Hayes Process Macro Model 4 (to test the mediating effect). Results: Nursing practice environment showed a significant positive correlation with nursing performance, retention intention, and nursing professional pride. Nursing practice performance showed a positive correlation with retention intention and nursing professional pride, and retention intention showed a significant positive correlation with nursing professional pride. The mediating effect of nursing professional pride was found in the effect of nurses' nursing practice environment on their retention intention. In addition, the mediating effect of nursing professional pride was found in the effect of nurses' nursing practice performance on their retention intention. Conclusion Through this study, it was confirmed that nursing professional pride is a major A factor affecting retention intention in the hospital. Therefore, in order to increase nurses' retention intention to remain in Hospital, the basis of basic data was presented for strategy development.

Purpose: Claudin 18.2 (CLDN18.2) has emerged as a promising therapeutic target for CLDN18.2-expressing gastric cancer (GC). We sought to examine the heterogeneity of CLDN18.2 expression between primary GC (PGC) and metastatic GC (MGC) using various scoring methods. Materials and Methods: We retrospectively analyzed data from 102 patients with pathologically confirmed paired primary and metastatic gastric or gastroesophageal junction adenocarcinomas. CLDN18.2 expression was evaluated through immunohistochemistry on formalin-fixed paraffin-embedded tissue samples. We assessed CLDN18.2 positivity using multiple scoring approaches, including the immunoreactivity score, H-score, and the percentage of tumor cells showing moderate-to-strong staining intensity. We analyzed the concordance rates between PGC and MGC and the association of CLDN18.2 positivity with clinicopathological features. Results: CLDN18.2 positivity varied from 25% to 65% depending on the scoring method, with PGC consistently showing higher expression levels than MGC. Intratumoral heterogeneity was noted in 25.5% of PGCs and 19.6% of MGCs. Intertumoral heterogeneity, manifesting as discordance in CLDN18.2 positivity between PGC and MGC, was observed in about 20% of cases, with moderate agreement across scoring methods (κ=0.47 to 0.60).In PGC, higher CLDN18.2 positivity correlated with synchronous metastasis, presence of peritoneal metastasis, poorly differentiated grade, and biopsy specimens. In MGC, positivity was associated with synchronous metastasis, presence of peritoneal metastasis, and metastatic peritoneal tissues. Conclusions CLDN18.2 expression demonstrates significant heterogeneity between PGC and MGC, with a 20% discordance rate. Comprehensive tissue sampling and reassessment of CLDN18.2 status are crucial, especially before initiating CLDN18.2-targeted therapies.

Background: and Purpose: Plasma biomarkers, including phosphorylated tau (ptau217), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL), are promising tools for detecting Alzheimer’s disease (AD) pathology. However, cross-laboratory reproducibility remains a challenge, even when using identical analytical platforms such as single-molecule array (Simoa). This study aimed to compare plasma biomarker measurements (ptau217, GFAP, and NfL) between 2 laboratories, the University of Gothenburg (UGOT) and DNAlink, and evaluate their associations with amyloid positron emission tomography (PET) imaging. Methods: Plasma biomarkers were measured using Simoa platforms at both laboratories:the UGOT and DNAlink Incorporation. Diagnostic performance for predicting amyloid PET positivity, cross-laboratory agreement, and the impact of normalization techniques were assessed. Bland-Altman plots and correlation analyses were employed to evaluate agreement and variability. Results: Plasma ptau217 concentrations exhibited strong correlations with amyloid PET global centiloid values, with comparable diagnostic performance between laboratories (area under the curve=0.94 for UGOT and 0.95 for DNAlink). Cross-laboratory agreement for ptau217 was excellent (r=0.96), improving further after natural log transformation. GFAP and NfL also demonstrated moderate to strong correlations (r=0.86 for GFAP and r=0.99 for NfL), with normalization reducing variability. Conclusions Plasma biomarker measurements were consistent across laboratories using identical Simoa platforms, with strong diagnostic performance and improved agreement after normalization. These findings support the scalability of plasma biomarkers for multicenter studies and underscore their potential for standardized applications in AD research and clinical practice.