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.

Sudden death during or after percutaneous coronary intervention (PCI) could be led to potential medicolegal disputes. This study aimed to investigate the clinical and postmortem findings in PCI-related deaths-focusing on the current statusto inform preventive strategies against these fatalities. Forty-three cases were retrieved from the National Forensic Service's postmortem records between 2015 and 2021, and the corresponding postmortem findings and clinical information were analyzed. The analyses revealed a relatively consistent annual incidence of PCI-related deaths. Immediate deaths during or shortly after PCI occurred in 17 cases (39.5%), and delayed PCI-related deaths after discharge from the hospital occurred in 26 cases (60.5%). The causes of PCI-related deaths in the postmortem cases were categorized into four groups: PCI complications (11 cases, 26%), acute myocardial infarction (23 cases, 53%), ischemic heart disease (8 cases, 19%), and others (1 case, 2%). Postmortem examinations played a critical role in determining the cause of death and obtaining medical evidence, including pathological findings of the heart as well as those of coronary artery and stent insertion. Our findings suggest that a detailed examination of the heart, coronary arteries, stent status, and atherosclerosis in PCI-related deaths could help provide more accurate information as medical evidence and prevent/resolve potential medicolegal issues. Further, this could advance our understanding of PCI-related deaths and inform future preventive strategies.

The increased expression of receptors for advanced glycation endproduct (RAGE) is known as a key player in the progression of vascular remodeling.However, the precise signal pathways regulating RAGE expression in vascular smooth muscle cells (VSMCs) in the injured vasculatures are unclear. Given the importance of mitogen-activated protein kinase (MAPK) signaling in cell proliferation, we investigated the importance of MAPK signaling in high-mobility group box 1 (HMGB1)-induced RAGE expression in VSMCs. In HMGB1 (100 ng/ml)-stimulated human VSMCs, the expression of RAGE mRNA and protein was increased in association with an increase in AGE-induced VSMC proliferation. The HMGB1-induced RAGE expression was attenuated in cells pretreated with inhibitors for ERK (PD98059, 10 μM) and p38 MAPK (SB203580, 10 μM) as well as in cells deficient in ERK and p38 MAPK using siRNAs, but not in cells deficient of JNK signaling. In cells stimulated with HMGB1, the phosphorylation of ERK, JNK, and p38 MAPK was increased. This increase in ERK and p38 MAPK phosphorylation was inhibited by p38 MAPK and ERK inhibitors, respectively, but not by JNK inhibitor. Moreover, AGE-induced VSMC proliferation in HMGB1-stimulated cells was attenuated in cells treated with ERK and p38 MAPK inhibitors. Taken together, our results indicate that ERK and p38 MAPK signaling are involved in RAGE expression in HMGB1-stimulated VSMCs. Thus, the ERK/p38 MAPKRAGE signaling axis in VSMCs was suggested as a potential therapeutic target for vascular remodeling in the injured vasculatures.

Objectives: The Korea Centers for Disease Control and Prevention has published “A Guideline for Unknown Disease Outbreaks (UDO).” The aim of this report was to introduce tabletop exercises (TTX) to prepare for UDO in the future. Methods: The UDO Laboratory Analyses Task Force in Korea Centers for Disease Control and Prevention in April 2018, assigned unknown diseases into 5 syndromes, designed an algorithm for diagnosis, and made a panel list for diagnosis by exclusion. Using the guidelines and laboratory analyses for UDO, TTX were introduced. Results: Since September 9th , 2018, the UDO Laboratory Analyses Task Force has been preparing TTX based on a scenario of an outbreak caused by a novel coronavirus. In December 2019, through TTX, individual missions, epidemiological investigations, sample treatments, diagnosis by exclusions, and next generation sequencing analysis were discussed, and a novel coronavirus was identified as the causal pathogen. Conclusion Guideline and laboratory analyses for UDO successfully applied in TTX. Conclusions drawn from TTX could be applied effectively in the analyses for the initial response to COVID-19, an ongoing epidemic of 2019 - 2020. Therefore, TTX should continuously be conducted for the response and preparation against UDO.

Robots are being used to assist the recovery of walking ability for patients with neurologic disorders. This study aimed to evaluate the feasibility and functional improvement of training with robot-assisted gait training (RAGT) using the Morning Walk®, an end-effector type robot using footplates and saddle seat support. A total of 189 individuals (65.1% men, 34.9% women; mean age, 53.2 years; age range: 5–87 years) with brain lesions, spinal cord injuries, Parkinson's disease, peripheral neuropathies, and pediatric patients were involved in this retrospectively registered clinical trial. Each participant performed 30 minutes of RAGT, five times a week, for a total of 24 sessions. Failure was defined as an inability to complete all 24 sessions, and the reasons for discontinuation were analyzed. Parameters of Medical Research Council scales and Functional Ambulation Categories were analyzed before and after RAGT training. Among the 189 patients, 22 (11.6%) failed to complete the RAGT. The reasons included decreased cooperation, musculoskeletal pain, saddle seat discomfort, excessive body-weight support, joint spasticity or restricted joint motion, urinary incontinence from an indwelling urinary catheter, and fatigue. Comparison between the pre- and post-training motor and ambulatory functions showed significant improvement. The result of the study indicates that the Morning Walk® is feasible and safe and useful for functional improvement in patients with various neurologic disordersTRIAL REGISTRATION: Clinical Research Information Service Identifier: KCT0003627

Purpose: This study examined the effects of training using the slime on the hand function and problem behavior in adults with intellectual disabilities. Methods: Five adults with intellectual disabilities who were judged as Level 1 or Level 2 were selected. The training consisted of one session (50 min)/day, one day/week for 10 weeks (preparatory activities: 5 min, slime activities: 40 min, finishing activities: 5 min) The functions of the hand (Purdue Pegboard Test [PPT], grip strength test [GST]) and problem behavior (Korean Behavior Problems Inventory [K-BPI]) were assessed three times (first assessment; pre, second assessment; post-5 weeks, third assessment; post-10 weeks). Wilcoxon signed-rank tests were used to analyze changes in the subjects’ performance before and after the intervention. Results: In the PPT, the use of the non-dominant hand was increased significantly (p<0.05). No significant differences were observed in the remaining results, but the mean value increased. In addition, although the problem behavior was not statistically significant, an increase in the problem behavior score was noted. Conclusion The training using the slime for adults with intellectual disabilities is effective in hand function improvement and reducing problem behavior.

Purpose: This study examined the effects of training using the slime on the hand function and problem behavior in adults with intellectual disabilities. Methods: Five adults with intellectual disabilities who were judged as Level 1 or Level 2 were selected. The training consisted of one session (50 min)/day, one day/week for 10 weeks (preparatory activities: 5 min, slime activities: 40 min, finishing activities: 5 min) The functions of the hand (Purdue Pegboard Test [PPT], grip strength test [GST]) and problem behavior (Korean Behavior Problems Inventory [K-BPI]) were assessed three times (first assessment; pre, second assessment; post-5 weeks, third assessment; post-10 weeks). Wilcoxon signed-rank tests were used to analyze changes in the subjects’ performance before and after the intervention. Results: In the PPT, the use of the non-dominant hand was increased significantly (p<0.05). No significant differences were observed in the remaining results, but the mean value increased. In addition, although the problem behavior was not statistically significant, an increase in the problem behavior score was noted. Conclusion The training using the slime for adults with intellectual disabilities is effective in hand function improvement and reducing problem behavior.