Background and Objectives: Graves’ ophthalmopathy (GO) is an autoimmune inflammatory disorder that can adversely affect quality of life in patients with Graves’ disease (GD). The objective of this study was to characterize the clinical features of patients with GO and to identify risk factors associated with its development and the need for anti-inflammatory treatment. Materials and Methods: In this multicenter, retrospective observational study, 818 patients with GD were identified via electronic medical record review. Clinical characteristics were assessed, and logistic regression analyses were performed to identify risk factors for GO development and the need for anti-inflammatory treatment. Results: Among the 818 patients with GD, 135 (16.5%) developed GO, and 60 (7.3%) of these patients received anti-inflammatory treatment. GO was diagnosed at the time of GD diagnosis in 54.8% of cases, and proptosis and eyelid/orbital swelling were the common presenting features. In multivariable analysis, female sex (odds ratio [OR]: 1.75, confidence interval [CI]: 1.02-3.03), goiter (OR: 1.71, CI: 1.08-2.71), and smoking (ex-smokers: OR, 2.18; 95% CI: 1.02-4.65; current smokers: OR, 3.11; CI, 1.78-5.44) were independently associated with GO development, whereas diabetes (OR: 0.35, CI: 0.14-0.89) was inversely associated. Higher total cholesterol (OR: 1.31, CI: 1.01-1.04) and elevated thyrotropin-binding inhibitory immunoglobulin levels (OR: 1.07, CI: 1.02-1.11) were also significantly associated with the need for anti-inflammatory treatment. Conclusion This study delineated the clinical features of GO and identified risk factors for its development and the need for anti-inflammatory treatment in patients with GD, providing valuable information for the management of GO in Korean patients.

Background: Transthoracic echocardiography (TTE) requires time-intensive integration of quantitative measure‑ ments and qualitative visual assessment. Fully automated artificial intelligence (AI)-based analysis may reduce total analysis time while preserving accuracy, but systematic real-world validation remains limited. Methods: This prospective, single-center pilot study enrolled 40 TTE examinations. Identical deidentified DICOM datasets were independently provided to a trained cardiac sonographer and a fully automated AI system comprising quantitative and qualitative visual interpretation modules. All outputs were compared with a cardiologist-adjudicated reference standard. Primary endpoints were total analysis time and noninferiority of AI-derived left ventricular ejection fraction (LVEF) versus the reference standard, with a prespecified margin of 3 percentage points (one-sided α = 0.025). Results: Median analysis time was 94 s (interquartile range [IQR], 82–106 s) for the AI workflow versus 490 s (IQR, 438–626 s) for the human workflow (P < 0.001). AI-derived LVEF met the noninferiority criterion (mean difference, 0.00 percentage points; upper one-sided 95% confidence bound, 1.41 percentage points; P < 0.001), with an intraclass correlation coefficient (ICC) of 0.902 (95% confidence interval, 0.822–0.947). ICCs for secondary quantitative indi‑ ces ranged from 0.625 to 0.989. For aortic regurgitation severity grading, AI’s overall accuracy was 75.0% (quadratic weighted κ = 0.762), compared with 82.5% for human interpretation (κ = 0.812, McNemar P = 0.579). Conclusions Fully automated AI-assisted TTE analysis substantially reduced total analysis time while maintaining noninferior LVEF accuracy and acceptable performance across secondary quantitative and qualitative indices. These findings support the use of AI as a practical workflow accelerator in routine echocardiography.

The purpose of this study was to identify predisposing, precipitating, and environmental factors related to the severity of delirium among older adults in a neurologic intensive care unit (ICU) based on the clinical practice guideline. Methods: A retrospective study was conducted using electronic medical record data from 945 older adults admitted to the neurological ICU at a tertiary hospital in Seoul, South Korea from January 2020 to July 2023. Delirium and subsyndromal delirium were evaluated using the Intensive Care Delirium Screening Checklist. Statistical analyses were performed using one-way ANOVA, χ2 tests, and multinominal logistic regression using the SPSS/WIN 26.0 program. Results: Compared with the normal group, a multinominal analysis revealed factors significantly associated with both delirium and subsyndromal delirium, including physical function, the length of ICU stay, dexmedetomidine use, anticonvulsants use, and physical restraints. However, compared to the normal group, ICU stay emerged as the most strongly related factor to delirium, whereas physical function was identified as the most significant factor related to subsyndromal delirium. These findings suggest that factors associated with delirium vary by its severity. Conclusion: Understanding the factors associated with delirium and subsyndromal delirium is crucial for clinicians. Pre-classification of high-risk groups based on the delirium severity among critically ill older adults is essential for providing high-quality delirium prevention.

Purpose: This paper aimed to investigate the levels and predictors of moral distress, compassion fatigue, and compassion satisfaction among nurses caring for patients with cancer and to identify predictors for the variables. Methods: A cross-sectional, descriptive correlational study was conducted on 245 nurses from hospitals in South Korea. Data was collected through online surveys from May to June 2025. Variables were measured using the Korean version of the Moral Distress Scale-Revised and the Professional Quality of Life Scale-5. Data were analyzed using a t-test, ANOVA, Pearson’s correlation, and multiple regression analysis. Results: Nurses reported moderate-to-high levels of moral distress, compassion fatigue, and compassion satisfaction, with religious affiliation predicting lower moral distress. Nurses with 3~5 years of experience caring for cancer patients exhibited lower moral distress than those with less than 3 years of experience. Employment in tertiary hospitals and the availability of support programs were predictors of lower moral distress, while caring for cancer patients throughout one’s career predicted higher moral distress. Advanced practice nurses, nurses providing advanced clinical support, and nurses who had completed self-care education were predictors of greater compassion fatigue. In contrast, religious affiliation, having more than five years of nursing experience, and possession of additional oncology nursing certifications significantly explained the variance in compassion satisfaction among nurses. Conclusion Moral distress, compassion fatigue, and compassion satisfaction varied by nurses’ personal and professional characteristics. Multilevel interventions, including structured self-care education and institutional support systems, are needed to alleviate emotional burden and promote professional well-being among oncology nurses.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Purpose: : Post-intensive care syndrome (PICS) is characterized by a constellation of mental health, physical, and cognitive impairments, and is recognized as a long-term sequela among survivors of intensive care units (ICUs). The objective of this study was to explore the impact of intensive care experience (ICE) on the development of PICS in individuals surviving critical care. Methods: : This secondary analysis utilized data derived from a prospective, multicenter cohort study of ICU survivors. The cohort comprised 143 survivors who were enrolled between July and August 2019. The original study's participants completed the Korean version of the ICE questionnaire (K-ICEQ) within one week following discharge from the ICU. Of these, 82 individuals completed the PICS questionnaire (PICSQ) three months subsequent to discharge from hospital. The influence of ICE on the manifestation of PICS was examined through Partial Least Squares-Structural Equation Modeling (PLS-SEM). Results: : The R2 values of the final model ranged from 0.35 to 0.51, while the Q2 values were all greater than 0, indicating adequacy for prediction of PICS. Notable pathways in the relationship between the four ICE dimensions and the three PICS domains included significant associations from ‘ICE-awareness of surroundings’ to ‘PICS-cognitive’, from ‘ICE-recall of experience’ to ‘PICS-cognitive’, and from ‘ICE-frightening experiences’ to ‘PICS-mental health’. Analysis found no significant moderating effects of age or disease severity on these relationships. Additionally, gender differences were identified in the significant pathways within the model. Conclusion : Adverse ICU experiences may detrimentally impact the cognitive and mental health domains of PICS following discharge. In order to improve long-term outcomes of individuals who survive critical care, it is imperative to develop nursing interventions aimed at enhancing the ICU experience for patients.

Background: The implication of the presence of tumor-infiltrating T lymphocytes (TIL-T) in diffuse large B-cell lymphoma (DLBCL) is yet to be elucidated. We aimed to investigate the effect of TIL-T levels on the prognosis of patients with DLBCL. Methods: Ninety-six patients with DLBCL were enrolled in the study. The TIL-T ratio was measured using QuPath, a digital pathology software package. The TIL-T ratio was investigated in three foci (highest, intermediate, and lowest) for each case, resulting in TIL-T–Max, TIL-T–Intermediate, and TIL-T–Min. The relationship between the TIL-T ratios and prognosis was investigated. Results: When 19% was used as the cutoff value for TIL-T–Max, 72 (75.0%) and 24 (25.0%) patients had high and low TIL-T–Max, respectively. A high TIL-T–Max was significantly associated with lower serum lactate dehydrogenase levels (p < .001), with patient group who achieved complete remission after RCHOP therapy (p < .001), and a low-risk revised International Prognostic Index score (p < .001). Univariate analysis showed that patients with a low TIL-T–Max had a significantly worse prognosis in overall survival compared to those with a high TIL-T–Max (p < .001); this difference remained significant in a multivariate analysis with Cox proportional hazards (hazard ratio, 7.55; 95% confidence interval, 2.54 to 22.42; p < .001). Conclusions Patients with DLBCL with a high TIL-T–Max showed significantly better prognosis than those with a low TIL-T–Max, and the TIL-T–Max was an independent indicator of overall survival. These results suggest that evaluating TIL-T ratios using a digital pathology system is useful in predicting the prognosis of patients with DLBCL.

BACKGROUND/OBJECTIVES: Chronic alcohol consumption causes oxidative stress in the body, which may accumulate excessively and cause a decline in memory; problem-solving, learning, and exercise abilities; and permanent damage to brain structure and function.Consequently, chronic alcohol consumption can cause alcohol-related diseases.MATERIALS/METHODS: In this study, the protective effects of Phyllostachys edulis (Carrière) J. Houz (PE) against alcohol-induced neuroinflammation and cognitive impairment were evaluated using a mouse model. Alcohol (16%, 5 g/kg/day for 6 weeks) and PE (100, 250, and 500 mg/kg/day for 21 days) were administered intragastrically to mice. RESULTS: PE showed a protective effect against memory deficits and cognitive dysfunction caused by alcohol consumption, confirmed through behavioral tests such as the T-maze, object recognition, and Morris water maze tests. Additionally, PE attenuated oxidative stress by reducing lipid oxidation, nitric oxide, and reactive oxygen species levels in the mice’s brains, livers, and kidneys. Improvement of neurotrophic factors and downregulation of apoptosis-related proteins were confirmed in the brains of mice fed low and medium concentrations of PE. Additionally, expression of antioxidant enzyme-related proteins GPx-1 and SOD-1 was enhanced in the liver of PE-treated mice, related to their inhibitory effect on oxidative stress. CONCLUSION This suggests that PE has both neuroregenerative and antioxidant effects.Collectively, these behavioral and histological results confirmed that PE could improve alcohol-induced cognitive deficits through brain neurotrophic and apoptosis protection and modulation of oxidative stress.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.