Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Background: s/Aims: In hepatocellular carcinoma (HCC), which exhibits high mortality and recurrence rates globally, the traits of cancer stem cells (CSCs) that significantly influence recurrence and metastasis are not well understood. CSCs are self-renewing cell types identified in most liquid and solid cancers, contributing to tumor initiation, growth, resistance, recurrence, and metastasis following chemo-radiotherapy or trans-arterial chemoembolization therapy. Methods: CSCs are classified based on the expression of cell surface markers such as CD133, which varies depending on the tumor type. Proteomic analysis of liver cancer cell lines with cancer stem cell potential and HCC cancer cell lines lacking stem cell propensity was conducted to compare and analyze specific expression patterns. Results: Proteomic profiling and enrichment analysis revealed higher expression of the calcium-binding protein S100 family in CD133+ Huh7 cells than in CD133- or wild-type cells. Furthermore, elevated expression of S100 family members was confirmed in an actual CD133+ liver cancer cell line via protein-protein network analysis and quantitative polymerase chain reaction (qPCR). Conclusion The S100 family members are not only new markers of cancer stem cells but will also assist in identifying new treatment strategies for CSC metastasis and tumor advancement.

Objective: We aimed to investigate the associations of the triglyceride-glucose index, which measures insulin resistance, and the incidence of Parkinson’s disease. Methods: Our study used the Health Screening Cohort database of the National Health Insurance Service of South Korea (2002–2019). We included 310,021 participants who had no previous history of Parkinson’s disease and for whom more than 3 triglyceride-glucose index measurements were available. A diagnosis of Parkinson’s disease was determined via the International Classification of Diseases Tenth edition (G20) with a specific reimbursement code for rare intractable diseases and a history of prescriptions for anti-Parkinsonism drugs. Results: During a median of 9.64 years (interquartile range 8.72–10.53), 4,587 individuals (1.5%) had Parkinson’s disease. Based on a multivariable time-dependent Cox proportional hazards model, a per-unit increase in triglyceride-glucose index score was associated with a significantly increased risk of Parkinson’s disease (hazard ratio [HR]: 1.062; 95% confidence interval [CI] 1.007–1.119). In a sensitivity analysis, the triglyceride-glucose index was associated with the incidence of Parkinson’s disease in a non–diabetes mellitus cohort (HR: 1.093; 95% CI 1.025–1.165), but not in the diabetes mellitus cohort (HR: 0.990; 95% CI 0.902–1.087). In a restricted cubic spline analysis, the association between the triglyceride-glucose index and the incidence risk of Parkinson’s disease showed a nonlinear increasing (J-shaped) trend. Conclusion Our study demonstrated that higher triglyceride-glucose index scores were associated with the incidence of Parkinson’s disease in the general population, particularly in a nondiabetic mellitus cohort.

Background and Objectives: Outcomes of deferring percutaneous coronary intervention (PCI) without invasive physiologic assessment for intermediate coronary lesions is uncertain.We sought to compare long-term outcomes between medical treatment and PCI of intermediate lesions without invasive physiologic assessment. Methods: A total of 899 patients with intermediate coronary lesions between 50% and 70% diameter-stenosis were randomized to the conservative group (n=449) or the aggressive group (n=450). For intermediate lesions, PCI was performed in the aggressive group, but was deferred in the conservative group. The primary endpoint was major adverse cardiac events (MACE, a composite of all-cause death, myocardial infarction [MI], or ischemia-driven any revascularization) at 3 years. Results: The number of treated lesions per patient was 0.8±0.9 in the conservative group and 1.7±0.9 in the aggressive group (p=0.001). At 3 years, the conservative group had a significantly higher incidence of MACE than the aggressive group (13.8% vs. 9.3%; hazard ratio [HR], 1.49; 95% confidence interval [CI], 1.00–2.21; p=0.049), mainly driven by revascularization of target intermediate lesion (6.5% vs. 1.1%; HR, 5.69; 95% CI, 2.20–14.73;p<0.001). Between 1 and 3 years after the index procedure, compared to the aggressive group, the conservative group had significantly higher incidence of cardiac death or MI (3.2% vs.0.7%; HR, 4.34; 95% CI, 1.24–15.22; p=0.022) and ischemia-driven any revascularization. Conclusions For intermediate lesions, medical therapy alone, guided only by angiography, was associated with a higher risk of MACE at 3 years compared with performing PCI, mainly due to increased revascularization.

Background: Skipping breakfast is associated with an increased risk of chronic inflammatory diseases. This study aimed to examine the association between breakfast-eating habits and inflammation, using high-sensitivity C-reactive protein (hs-CRP) as a marker. Methods: A total of 4,000 Korean adult males with no history of myocardial infarction, angina, stroke, diabetes, rheumatoid arthritis, cancer, or current smoking were included. Data from the 2016–2018 Korea National Health and Nutrition Examination Survey were used for analysis. The frequency of breakfast consumption was assessed through a questionnaire item in the dietary survey section asking participants about their weekly breakfast consumption routines over the past year. Participants were categorized into two groups, namely “0–2 breakfasts per week” and “3–7 breakfasts per week”; hs-CRP concentrations were measured through blood tests. Results: Comparing between the “infrequent breakfast consumption (0–2 breakfasts per week)” and “frequent breakfast consumption (3–7 breakfasts per week)” groups, the mean hs-CRP was found to be significantly higher in the “infrequent breakfast consumption” group, even after adjusting for age, body mass index, physical activity, alcohol consumption, systolic blood pressure, blood pressure medication, fasting blood glucose, and triglycerides (mean hs-CRP: frequent breakfast consumption, 1.36±0.09 mg/L; infrequent breakfast consumption, 1.17±0.05 mg/L; P-value=0.036). Conclusion Less frequent breakfast consumption was associated with elevated hs-CRP levels. Further large-scale studies incorporating adjusted measures of daily eating patterns as well as food quality and quantity are required for a deeper understanding of the role of breakfast in the primary prevention of chronic inflammatory diseases.

Objective: To establish local diagnostic reference levels (DRLs) for pediatric neck CT based on age, weight, and water-equivalent diameter (WED) across multiple university hospitals in South Korea. Materials and Methods: This retrospective study analyzed pediatric neck CT examinations from nine university hospitals, involving patients aged 0–18 years. Data were categorized by age, weight, and WED, and radiation dose metrics, including volume CT dose index (CTDIvol) and dose length product, were recorded. Data retrieval and analysis were conducted using a commercially available dose-management system (Radimetrics, Bayer Healthcare). Local DRLs were established following the International Commission on Radiological Protection guidelines, using the 75th percentile as the reference value. Results: A total of 1159 CT examinations were analyzed, including 169 scans from Institution 1, 132 from Institution 2, 126 from Institution 3, 129 from Institution 4, 128 from Institution 5, 105 from Institution 6, 162 from Institution 7, 127 from Institution 8, and 81 from Institution 9. Radiation dose metrics increased with age, weight, and WED, showing significant variability both within and across institutions. For patients weighing less than 10 kg, the DRL for CTDIvol was 5.2 mGy. In the 10–19 kg group, the DRL was 5.8 mGy; in the 20–39 kg group, 7.6 mGy; in the 40–59 kg group, 11.0 mGy; and for patients weighing 60 kg or more, 16.2 mGy. DRLs for CTDIvol by age groups were as follows: 5.3 mGy for infants under 1 year, 5.7 mGy for children aged 1–4 years, 7.6 mGy for ages 5–9 years, 11.2 mGy for ages 10–14 years, and 15.6 mGy for patients 15 years or older. Conclusion Local DRLs for pediatric neck CT were established based on age, weight, and WED across nine university hospitals in South Korea.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Background: The aim of this study is to evaluate the prevalence and associated psychosocial factors of behavioral addictions among school adolescents living in the Gwangju and Jeonnam regions in Korea. Methods: A self-reported survey was conducted from December 4, 2023, to January 31, 2024, including 855 middle and high school students residing in the Gwangju-Jeonnam regions. Aside from the information on demographic characteristics, data on depression, anxiety, Internet gaming addiction, gambling problems, and resilience was obtained. Results: The prevalence of Internet gaming addiction among adolescents was 5.4%, while the prevalence of gambling problems was 3.3%. The male adolescents had a significantly higher risk of behavioral addiction compared with the female adolescents. The logistic regression analysis revealed that male and depression were significant risk factors for Internet gaming addiction. For gambling problems, male was identified as a significant risk factor. Conclusion The findings of this study suggested that the prevalence of behavioral addiction among school adolescents has been relatively higher than that of previous studies, emphasizing the need for community-based prevention and intervention strategies tailored to the sex difference and psychological factors associated with adolescent behavioral addictions.