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.

Objective: To analyze the microbiological and clinical characteristics of vulvovaginitis in girls, distinguishing between the premenarcheal and postmenarcheal groups in a tertiary center in South Korea. Methods: This retrospective cohort study included 195 patients under 20 years of age diagnosed with vulvovaginitis at a tertiary hospital between 2014 and 2023. The patients were categorized into premenarcheal (n=95) and postmenarcheal (n=100) groups. Data on initial symptoms, microbial cultures, and treatment methods were analyzed. Results: The most common initial symptom was vaginal discharge, reported in 63.1% of cases. Culture results showed a 51.3% positivity rate for any microorganism, with a prevalence of gram-negative rods (32.8%) and gram-positive cocci (14.4%). The most frequently isolated microorganisms were Escherichia coli (17.9%), Candida albicans (7.7%), and Enterococcus faecalis (6.7%). Gram-negative rods were more common in the premenarcheal group (37.1% vs. 25.0%; p=0.01). No significant differences were observed in the prevalence of gram-positive cocci and Candida species between the two groups (16.8% vs. 12.0%, p=0.22; 6.3% vs. 13.0%, p=0.09; respectively). The susceptibilities of grampositive microorganisms to penicillin, oxacillin, clindamycin, vancomycin, and tetracycline were 58.8%, 58.3%, 94.7%, 100.0%, and 73.7%, respectively. The susceptibilities of gram-negative microorganisms to amoxicillin/clavulanic acid, ciprofloxacin, ceftriaxone, and nitrofurantoin were 89.3%, 85.3%, 76.0%, and 100.0%, respectively. Conclusion This study identified differences in the microbial profiles associated with vulvovaginitis between premenarcheal and postmenarcheal girls. Age-specific and history-based clinical approaches tailored to menarcheal status are warranted to improve the management and outcomes of pediatric and adolescent vulvovaginitis.

Reliable preclinical models for assessing drug-induced cardiotoxicity are essential to reduce the high rate of drug withdrawals during development. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have emerged as a promising platform for such assessments due to their expression of cardiacspecific ion channels and electrophysiological properties. In this study, we investigated the effects of eight arrhythmogenic drugs—E4031, nifedipine, mexiletine, JNJ303, flecainide, moxifloxacin, quinidine, and ranolazine—on hiPSC-CMs derived from both healthy individuals and a long QT syndrome (LQTS) patient using multielectrode array systems. The results demonstrated dose-dependent changes in field potential duration and arrhythmogenic risk, with LQTS-derived hiPSC-CMs showing increased sensitivity to hERG channel blockers such as E4031. Furthermore, the study highlights the potential of hiPSC-CMs to model disease-specific cardiac responses, providing insights into genetic predispositions and personalized drug responses.Despite challenges related to the immaturity of hiPSC-CMs, their ability to recapitulate human cardiac electrophysiology makes them a valuable tool for preclinical cardiotoxicity assessments. This study underscores the utility of integrating patientderived hiPSC-CMs with advanced analytical platforms, such as multi-electrode array systems, to evaluate drug-induced electrophysiological changes. These findings reinforce the role of hiPSC-CMs in drug development, facilitating safer and more efficient screening methods while supporting precision medicine applications.

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: Respiratory infections play a major role in acute exacerbation of chronic obstructive pulmonary disease (AECOPD). This study assessed the prevalence of bacterial and viral pathogens and their clinical impact on patients with AECOPD. Methods: This retrospective study included 1,186 patients diagnosed with AECOPD at 28 hospitals in South Korea between 2015 and 2018. We evaluated the identification rates of pathogens, basic patient characteristics, clinical features, and the factors associated with infections by potentially drug-resistant (PDR) pathogens using various microbiological tests. Results: Bacteria, viruses, and both were detected in 262 (22.1%), 265 (22.5%), and 129 (10.9%) of patients, respectively. The most common pathogens included Pseudomonas aeruginosa (17.8%), Mycoplasma pneumoniae (11.2%), Streptococcus pneumoniae (9.0%), influenza A virus (19.0%), rhinovirus (15.8%), and respiratory syncytial virus (6.4%). Notably, a history of pulmonary tuberculosis (odds ratio [OR], 1.66; p=0.046), bronchiectasis (OR, 1.99; p=0.032), and the use of a triple inhaler regimen within the past 6 months (OR, 2.04; p=0.005) were identified as significant factors associated with infection by PDR pathogens. Moreover, patients infected with PDR pathogens exhibited extended hospital stays (15.9 days vs. 12.4 days, p=0.018) and higher intensive care unit admission rates (15.9% vs. 9.5%, p=0.030). Conclusion This study demonstrates that a variety of pathogens are involved in episodes of AECOPD. Nevertheless, additional research is required to confirm their role in the onset and progression of AECOPD.

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.

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: Colorectal carcinomas (CRCs) with caudal-type homeobox 2 (CDX2) loss are recognized to pursue an aggressive behavior but tend to be accompanied by a high density of tumor-infiltrating lymphocytes (TILs). However, little is known about whether there is an interplay between CDX2 loss and TIL density in the survival of patients with CRC. Methods: Stage III CRC tissues were assessed for CDX2 loss using immunohistochemistry and analyzed for their densities of CD8 TILs in both intraepithelial (iTILs) and stromal areas using a machine learning-based analytic method. Results: CDX2 loss was significantly associated with a higher density of CD8 TILs in both intraepithelial and stromal areas. Both CDX2 loss and a high CD8 iTIL density were found to be prognostic parameters and showed hazard ratios of 2.314 (1.050–5.100) and 0.378 (0.175–0.817), respectively, for cancer-specific survival. A subset of CRCs with retained CDX2 expression and a high density of CD8 iTILs showed the best clinical outcome (hazard ratio of 0.138 [0.023–0.826]), whereas a subset with CDX2 loss and a high density of CD8 iTILs exhibited the worst clinical outcome (15.781 [3.939–63.230]). Conclusions Altogether, a high density of CD8 iTILs did not make a difference in the survival of patients with CRC with CDX2 loss. The combination of CDX2 expression and intraepithelial CD8 TIL density was an independent prognostic marker in adjuvant chemotherapy-treated patients with stage III CRC.