Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

RESULTS: Resveratrol significantly reduced cardiac hypertrophy and remodeling in aging hearts. In addition, resveratrol significantly ameliorated aging-induced mitochondrial dysfunction (e.g., decreased oxygen respiration and increased hydrogen peroxide emission) and mitochondria-dependent apoptotic signaling (the Bax/Bcl-2 ratio, mitochondrial permeability transition pore opening sensitivity, and cleaved caspase-3 protein levels).Resveratrol also significantly attenuated aging-induced apoptosis (determined via cleaved caspase-3 staining and TUNEL-positive myonuclei) in cardiac muscles. CONCLUSION This study demonstrates that resveratrol treatment has a beneficial effect on aging-induced cardiac remodeling by ameliorating mitochondrial dysfunction and inhibiting mitochondria-mediated apoptosis in the heart.

Objective: In South Korea, there is a significant gap in systematic, evidence-based research on intensive case management (ICM) for individuals with severe mental illness (SMI). This study aims to evaluate the effectiveness of ICM through a randomized controlled trial (RCT) comparing ICM with standard case management (non-ICM). Methods: An RCT was conducted to assess the effectiveness of Seoul-intensive case management (S-ICM) vs. non-ICM in individuals with SMI in Seoul. A total of 78 participants were randomly assigned to either the S-ICM group (n=41) or the control group (n=37). Various clinical assessments, including the Brief Psychiatric Rating Scale (BPRS), Montgomery–Åsberg Depression Rating Scale, Health of the Nation Outcome Scale, and Clinical Global Impression-Improvement (CGI-I), along with quality-of-life measures such as the WHO Disability Assessment Schedule, WHO Quality of Life scale, and Multidimensional Scale of Perceived Social Support (MSPSS) were evaluated over a 3-month period. Statistical analyses, including analysis of covariance and logistic regression, were used to determine the effectiveness of S-ICM. Results: The S-ICM group had significantly lower odds of self-harm or suicidal attempts compared to the control group (adjusted odds ratio [aOR]=0.30, 95% confidence interval [CI]: 0.21–1.38). Psychiatric symptoms measured by the BPRS and perceived social support measured by the MSPSS significantly improved in the S-ICM group. The S-ICM group also had significantly higher odds of CGI-I compared to the control group (aOR=8.20, 95% CI: 2.66–25.32). Conclusion This study provides inaugural evidence on the effectiveness of S-ICM services, supporting their standardization and potential nationwide expansion.

Purpose: Identifying and managing risk factors for lower urinary tract symptoms (LUTS) is crucial because it impacts the quality of life of elderly individuals. Lifestyle factors, including physical activity (PA), and their relationship with LUTS have not been well studied. This objective of this study was to investigate the association between PA and LUTS. Materials and Methods: A total of 7,296 men were included in this cross-sectional study. PA was quantified in metabolic equivalent (MET)-hours per week, and LUTS severity was assessed using the international prostate symptom score. Logistic regression was used to analyze the association between PA and LUTS, including voiding and storage symptoms. Results: The average age of the participants was 57.8 years, and the prevalence of LUTS was 41.3%. After adjusting for potential confounders, PA was inversely associated with the prevalence and severity of moderate-to-severe LUTS, showing a dose-response pattern (all p for trend <0.01). Compared to the minimal activity group, which engaged in <5 MET-hours per week of PA, the odds ratios for moderate to severe LUTS were 0.83 (95% confidence interval [CI]: 0.72–0.97) for men engaging in 15–30 MET-hours per week, 0.82 (95% CI: 0.71–0.95) for 30–60 MET-hours per week, and 0.72 (95% CI: 0.62–0.84) for ≥60 MET-hours per week. The possible protective effect of PA was still observed in the additional analysis for voiding and storage symptoms showing the same dose-response pattern (all p for trend <0.01). Conclusions A higher PA level was associated with a lower prevalence and severity of total, voiding, and storage LUTS in a dose-dependent manner in Korean men.

Background: Lung ultrasound (LUS) has proven valuable in the initial assessment of coronavirus disease 2019 (COVID-19), but its role in detecting pulmonary fibrosis following intensive care remains unclear. This study aims to assess the presence of pulmonary sequelae and fibrosis-like changes using LUS in survivors of severe COVID-19 pneumonia one month after discharge. Methods: We prospectively enrolled patients with severe COVID-19 who required mechanical ventilation in the intensive care unit (ICU) and conducted LUS assessments from admission to the outpatient visit after discharge. We tracked changes in key LUS findings and applied our proprietary LUS scoring system. To evaluate LUS accuracy, we correlated measured LUS values with computed tomography scores. Results: We evaluated B-line presence, pleural thickness, and consolidation in 14 eligible patients. The LUS scores exhibited minimal changes, with values of 19.1, 19.2, and 17.5 at admission, discharge, and the outpatient visit, respectively. Notably, the number of B-lines decreased significantly, from 1.92 at admission to 0.56 at the outpatient visit (p<0.05), while pleural thickness increased significantly, from 2.05 at admission to 2.48 at the outpatient visit (p≤0.05). Conclusion This study demonstrates that LUS can track changes in lung abnormalities in severe COVID-19 patients from ICU admission through to outpatient follow-up. While pleural thickening and B-line patterns showed significant changes, no correlation was found between LUS and high-resolution computed tomography fibrosis scores. These findings suggest that LUS may serve as a supplementary tool for assessing pulmonary recovery in severe COVID-19 cases.

Japan has the highest proportion of older adults worldwide and has consistently strengthened its healthcare system to mitigate the medical and welfare burden associated with population aging. Primary care has been positioned as a key strategy for establishing a sustainable healthcare system within a super-aged society. Japan has pursued this goal through the implementation of community-based integrated care systems, the operation of a long-term care insurance system, the specialized training of primary care physicians, and the establishment of the “Comprehensive Medical Specialist” program. These initiatives aim to optimize the utilization of healthcare resources and ensure the long-term sustainability of the healthcare system. Japan’s experience offers critical insights for Korea, which requires effective strategies to strengthen its primary care system.

Purpose: This study aimed to empirically verify the impact of measurement model selection on research outcomes and their interpretation through an analysis of children’s emotional and social problems measured by the Pediatric Symptom Checklist (PSC) using both reflective and formative measurement models. These models were represented by covariance-based structural equation modeling (CB-SEM) and partial least squares SEM (PLS-SEM), respectively. Methods: This secondary data analysis evaluated children’s emotional and social problems as both reflective and formative constructs. Reflective models were analyzed using CB-SEM, while formative models were assessed using PLS-SEM. Comparisons between these two approaches were based on model fit and parameter estimates. Results: In the CB-SEM analysis, which assumed a reflective measurement model, a model was not identified due to inadequate fit indices and a Heywood case, indicating improper model specification. In contrast, the PLS-SEM analysis, assuming a formative measurement model, demonstrated adequate reliability and validity with significant path coefficients, supporting the appropriateness of the formative model for the PSC. Conclusion The findings indicate that the PSC is more appropriately analyzed as a formative measurement model using PLS-SEM, rather than as a reflective model using CB-SEM. This study highlights the necessity of selecting an appropriate measurement model based on the theoretical and empirical characteristics of constructs in nursing research. Future research should ensure that the nature of measurement variables is accurately reflected in the choice of statistical models to improve the validity of research outcomes.

Purpose: This study aimed to empirically verify the impact of measurement model selection on research outcomes and their interpretation through an analysis of children’s emotional and social problems measured by the Pediatric Symptom Checklist (PSC) using both reflective and formative measurement models. These models were represented by covariance-based structural equation modeling (CB-SEM) and partial least squares SEM (PLS-SEM), respectively. Methods: This secondary data analysis evaluated children’s emotional and social problems as both reflective and formative constructs. Reflective models were analyzed using CB-SEM, while formative models were assessed using PLS-SEM. Comparisons between these two approaches were based on model fit and parameter estimates. Results: In the CB-SEM analysis, which assumed a reflective measurement model, a model was not identified due to inadequate fit indices and a Heywood case, indicating improper model specification. In contrast, the PLS-SEM analysis, assuming a formative measurement model, demonstrated adequate reliability and validity with significant path coefficients, supporting the appropriateness of the formative model for the PSC. Conclusion The findings indicate that the PSC is more appropriately analyzed as a formative measurement model using PLS-SEM, rather than as a reflective model using CB-SEM. This study highlights the necessity of selecting an appropriate measurement model based on the theoretical and empirical characteristics of constructs in nursing research. Future research should ensure that the nature of measurement variables is accurately reflected in the choice of statistical models to improve the validity of research outcomes.