Purpose: To describe the incidence and mortality of upper tract urothelial carcinoma (UTUC) from 2002–2020 using data from the Korean National Health Insurance Service, which contains data from the entire Korean population. Materials and Methods: Reimbursement records for 43,255 patients diagnosed with primary UTUC (according to the International Classification of Disease 10th revision code C65 and C66) between 2002–2020 were retrieved. The study period was split into four: period I (2002–2005), period II (2006–2010), period III (2011–2015), and period IV (2016–2020). Trends were quantified by calculating the annual percentage change (APC). Mortality data were obtained from the Statistics Korea. Results: From 2002–2020, the incidence of UTUC in Korea increased gradually from 9.34 to 11.40 per 100,000 person-years. Although there was a male predominance, the male to female ratio did not change significantly over time; however, age at the time of diagnosis, the comorbidity index, and the proportion of patients undergoing open/laparoscopic surgery increased significantly over time. There was a modest improvement in 5-year survival (both all cause- and cancer-specific) over the study period. Multivariate analysis identified age at diagnosis, sex, the comorbidity index, and open/laparoscopic surgery as being associated with survival. Conclusions Between 2002 and 2020, the incidence of UTUC in Korea showed a general upward trend; however, survival outcomes have improved. These representative datasets from the Korean population might provide crucial information that enables clinicians to better understand of the epidemiology of UTUC in Korea.

Purpose: This study aimed to evaluate and optimize microbial DNA extraction methods from urine, a non-invasive sample source, to enhance DNA quality, purity, and reliability for urinary microbiome research and biomarker discovery in bladder cancer. Materials and Methods: A total of 302 individuals (258 with genitourinary cancers and 44 with benign urologic diseases) participated in this study. Urine samples were collected via sterile catheterization, resulting in 445 vials for microbial analysis. DNA extraction was performed using three protocols: the standard protocol (SP), water dilution protocol (WDP), and chelation-assisted protocol (CAP). DNA quality (concentration, purity, and contamination levels) was assessed using NanoDrop spectrophotometry.Microbial analysis was conducted on 138 samples (108 cancerous and 30 benign) using 16S rRNA sequencing. Prior to sequencing on the Illumina MiSeq platform, Victor 3 fluorometry was used for validation. Results: WDP outperformed other methods, achieving significantly higher 260/280 and 260/230 ratios, indicating superior DNA purity and reduced contamination, while maintaining reliable DNA yields. CAP was excluded due to poor performance across all metrics. Microbial abundance was significantly higher in WDP-extracted samples (p<0.0001), whereas SP demonstrated higher alpha diversity indices (p<0.01), likely due to improved detection of low-abundance taxa. Beta diversity analysis showed no significant compositional differences between SP and WDP (p=1.0), supporting the reliability of WDP for microbiome research. Conclusions WDP is a highly effective and reliable method for microbial DNA extraction from urine, ensuring high-quality and reproducible results. Future research should address sample variability and crystal precipitation to further refine microbiome-based diagnostics and therapeutics.

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.

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.

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.

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: This study aimed to develop and assess the feasibility and effectiveness of digital therapeutics for supportive care after gastrectomy.Materials and Method: The study included 39 patients with gastric cancer who underwent minimally invasive gastrectomy and were able to use a mobile application (app) on their smartphones. The developed research app automatically calculates and provides daily targets for calorie and protein intake based on the patient’s body mass index (BMI). Patients recorded their daily diets, weights, and symptoms in the app and completed special questionnaires to assess the feasibility of the app in real-world clinical practice. Results: At the 10-week follow-up, the mean questionnaire scores for ease of learning, usability, and effectiveness of the app (primary endpoint) were 2.32±0.41, 2.35±0.43, and 2.4±0.39 (range: 0–3), respectively. Patients were classified as underweight (<18.5, n=4), normal (18.5–24.9, n=24), or overweight (≥25.0, n=11) according to predischarge BMI.Underweight patients showed higher compliance with app usage and a higher rate of achieving the target calorie and protein intake than normal weight and overweight patients (98% vs. 77% vs. 81%, p=0.0313; 102% vs. 75% vs. 61%, P=0.0111; 106% vs. 79% vs. 64%, P=0.0429). Two patients transitioned from underweight to normal weight (50.0%), one patient (4.3%) transitioned from normal weight to underweight, and two patients (22.2%) transitioned from overweight to normal weight. Conclusions The mobile app is feasible and useful for postoperative supportive care in terms of ease of learning, usability, and effectiveness. Digital therapeutics may be an effective way to provide supportive care for postgastrectomy patients, particularly in terms of nutrition.

Purpose: In a preclinical study using a swine myocardial infarction (MI) model, a delayed enhancement (DE)-multi-detector computed tomography (MDCT) scan was performed using a hybrid system alongside diagnostic invasive coronary angiography (ICA) without the additional use of a contrast agent, and demonstrated an excellent correlation in the infarct area compared with histopathologic specimens. In the present investigation, we evaluated the feasibility and diagnostic accuracy of a myocardial viability assessment by DE-MDCT using a hybrid system comprising ICA and MDCT alongside diagnostic ICA without the additional use of a contrast agent. Materials and Methods: We prospectively enrolled 13 patients (median age: 67 years) with a previous MI (>6 months) scheduled to undergo ICA. All patients underwent cardiac magnetic resonance (CMR) imaging before diagnostic ICA. MDCT viability scans were performed concurrently with diagnostic ICA without the use of additional contrast. The total myocardial scar volume per patient and average transmurality per myocardial segment measured by DE-MDCT were compared with those from DE-CMR. Results: The DE volume measured by MDCT showed an excellent correlation with the volume measured by CMR (r=0.986, p<0.0001). The transmurality per segment by MDCT was well-correlated with CMR (r=0.900, p<0.0001); the diagnostic performance of MDCT in differentiating non-viable from viable myocardium using a 50% transmurality criterion was good with a sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 87.5%, 99.5%, 87.5%, 99.5%, and 99.1%, respectively. Conclusion The feasibility of the DE-MDCT viability assessment acquired simultaneously with conventional ICA was proven in patients with chronic MI using DE-CMR as the reference standard.

Background: The rupture of the central slip of an extensor tendon of a finger causes a boutonniere (or buttonhole) deformity, characterized by pathologic flexion at the proximal interphalangeal (PIP) joint and hyperextension at the distal interphalangeal (DIP) joint. Currently, there are no standard treatment guidelines for this deformity. This study aimed to report clinical results of surgery to correct chronic boutonniere deformity. Methods: This retrospective case series was conducted between January 2010 and December 2018 and only 13 patients with trauma-induced chronic deformity were included. After excision of elongated scar tissue, a direct anatomic end-to-end repair using a loop suture technique with supplemental suture anchor augmentation was conducted. Total active motion was assessed before and after surgery and self-satisfaction scores were collected from phone surveys. Results: All patients presented with Burton stage I deformities defined as supple and passively correctable joints. The initial mean extension lag of the PIP joint (43.5°) was improved by an average of 21.9° at the final follow-up (p < 0.001). The mean hyperextension of the DIP joint averaged 19.2° and improved by 0.8° flexion contracture (p < 0.001). The average total active motion was 220.4° (range, 160°–260°). Based on the Souter’s criteria, 69.2% (9/13) of the patients had good results. Only 1 patient reported fair outcome and 23.1% (3/13) reported poor outcome. The average Strickland formula score was 70 (range, 28.6–97.1). In total, 10 patients (77%) had excellent or good results. Of 10 patients contacted by phone, self-reported satisfaction score was very satisfied in 2, satisfied in 3, average in 3, poor in 1, and very poor in 1. Three patients reported a relapse of the deformity during range of motion exercises, 1 of whom underwent revision surgery. One patient complained of PIP joint flexion limitation, and 2 complained of DIP joint flexion limitation at final follow-up. Conclusions In chronic boutonniere deformity, central slip reconstruction with anchor suture augmentation can be an easily applicable surgical option, which offers fair to excellent outcome in 77% of the cases. The risk of residual extension lag and recurrence of deformity should be discussed prior to surgery.

Background: Coronavirus disease 2019 (COVID-19) outbreaks occur in hospitals in many parts of the world. In hospital settings, the possibility of airborne transmission needs to be investigated thoroughly. Materials and Methods: There was a nosocomial outbreak of COVID-19 in a hematologic ward in a tertiary hospital, Seoul, Korea. We found 11 patients and guardians with COVID-19 through vigorous contact tracing and closed-circuit television monitoring. We found one patient who probably had acquired COVID-19 through airborne-transmission. We performed airflow investigation with simulation software, whole-genome sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Results: Of the nine individuals with COVID-19 who had been in the hematologic ward, six stayed in one multi-patient room (Room 36), and other three stayed in different rooms (Room 1, 34, 35). Guardian in room 35 was close contact to cases in room 36, and patient in room 34 used the shared bathroom for teeth brushing 40 minutes after index used.Airflow simulation revealed that air was spread from the bathroom to the adjacent room 1 while patient in room 1 did not used the shared bathroom. Airflow was associated with poor ventilation in shared bathroom due to dysfunctioning air-exhaust, grill on the door of shared bathroom and the unintended negative pressure of adjacent room. Conclusion Transmission of SARS-CoV-2 in the hematologic ward occurred rapidly in the multi-patient room and shared bathroom settings. In addition, there was a case of possible airborne transmission due to unexpected airflow.