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.

Background: Exposure to endocrine disrupting chemicals (EDCs) that influence the hormonal and homeostatic systems is known to be associated with gynecologic health risks in many countries. In this study, we evaluated exposure to EDCs associated with diminished ovarian reserve (DOR) and gynecologic health risks. Methods: This cross-sectional study was performed from September 2014 to November 2014 and included 307 Korean reproductive-aged women. Anthropometric measurements, laboratory tests with urine and blood sampling and pelvic ultrasound examinations were performed. Results: Urinary bisphenol A (BLA) level was significantly higher in the DOR group with antiMüllerian hormone lower than 25 percentile (1.89 ± 2.17 ug/g and 1.58 ± 1.08 ug/g, P < 0.05).Urinary mono-(2-ethyl-5-hydroxyhexyl) phthalate, mono-(2-ethyl-5-oxohexyl) phthalate and mono-N-butyl phthalate, and substrates of phthalate were evaluated and no significant difference was observed between the DOR group and non-DOR group. Logistic regression analysis suggested an increase in infertility in high BPA exposure group and the odds ratio (OR, 4.248) was statistically significant after adjustment for age, birth control pills, and the age of menarche, parity, and waist circumference. High phthalate exposure was associated with endometrial polyp after adjustment (OR, 2.742). Conclusion BPA exposure might be associated with DOR and infertility. Meanwhile, endometrial polyp is increased in women with high phthalate exposure. Therefore, the risk of exposures to EDCs for reproduction should be a matter of concern in reproductive-aged women.

Purpose: The purpose of this study was to explore the parenting experiencesthat mothers of children with brain lesions with problems swallowing had. Methods: Data was collected through in-depth interviews with 5 mothers who have children with this disability who have a high risk of aspiration at mealtime. The key question in this study was “What is the nature of the burden that mothers of children with brain lesions who are at risk of aspiration experienced?”. The phenomenological analysis was applied. Results: Qualitative data were categorized into 18 themes, 6 theme clusters and 3 categories. These are the categories that were extracted: ‘Enduring the reality in which is difficult to accept’, ‘Whirlwind of emotions felt in the family’, ‘Wishes that cries out in the fog’. Conclusion It is necessary to develop a support program for mothers who take care of children with disabilities. Rehabilitation nursing plans should be established so that mothers of children with disabilities who are at risk of aspiration can strengthen their energy on their own.

Purpose: This study was conducted to explore the core educational components of interprofessional education (IPE) for pediatric emergencies to establish a basis for interprofessional simulation education. Methods: Using Whittemore and Knafl's integrative review method, we searched for studies in PubMed, Embase, Cochrane Library, CINAHL, and four South Korean databases (RISS, NDSL, DBpia, and KISS). Results: We identified 21 studies on the general characteristics of IPE in pediatric emergency situations and integrated the core components of IPE according to a PRISMA flowchart. Three core components were identified (individual - competent professionals, team - cooperative professions, and outcome - optimal achievement), with the subthemes of role and responsibility, clinical judgment, performance, leadership, communication, teamwork, patient safety, and quality improvement. Conclusion We recommend that IPE pediatric emergencies should contain the three dimensions of these core components to enhance individual and team performance and to promote optimal achievement in terms of patient outcomes. IPE programs should consider these characteristics and include a valid tool for evaluating the programs' effectiveness.

Purpose: We aimed to identify collaborative disaster governance through the demand and supply analysis of resources recognized by nurses during the COVID-19 pandemic. Methods: We used a descriptive study design with an online survey technique for data collection. The survey questions were developed based on focus group interviews with nurses responding to COVID-19 and expert validity testing. A 42-question online survey focusing on disaster governance was sent to nurses working in COVID-19 designated hospitals, public health offices, and schools. A total of 630 nurses participated in the survey. Demand and supply analysis was used to identify the specific components of disaster governance during a pandemic situation and analyze priority areas in disaster governance, as reported by nurses. Results: Demand and supply analysis showed that supplies procurement, cooperation, education, and environment factors clustered in the high demand and supply quadrant while labor condition, advocacy, emotional support, and workload adjustment factors clustered in the high demand but low supply quadrant, indicating a strong need in those areas of disaster governance among nurses. The nurses practicing at the public health offices and schools showed major components of disaster governance plotted in the second quadrant, indicating weak collaborative disaster governance. Conclusion These findings show that there is an unbalanced distribution among nurses, resulting in major challenges in collaborative disaster governance during COVID-19. In the future and current pandemic, collaborative disaster governance, through improved distribution, will be useful for helping nurses to access more required resources and achieve effective pandemic response.

The purpose of this in vivo study was to assess the clinical screening performance of a quantitative light-induced fluorescence (QLF) device in detecting proximal caries in primary molars. Fluorescence loss, red autofluorescence and a simplified QLF score for proximal caries (QS-proximal) were evaluated for their validity in detecting proximal caries in primary molars compared to bitewing radiography.
Three hundred and forty-four primary molar surfaces were included in the study. Carious lesions were scored according to lesion severity assessed by visual-tactile and radiographic examinations. The QLF images were analyzed for two quantitative parameters, fluorescence loss and red autofluorescence, as well as for QS-proximal. For both quantitative parameters and QS-proximal, the sensitivity, specificity and area under receiver operating curve (AUROC) were calculated as a function of the radiographic scoring index at enamel and dentin caries levels.
Both quantitative parameters showed fair AUROC values for detecting dentine level caries (△F = 0.794, △R = 0.750). QS-proximal showed higher AUROC values (0.757 - 0.769) than that of visual-tactile scores (0.653) in detecting dentine level caries.
The QLF device showed fair screening performance in detecting proximal caries in primary molars compared to bitewing radiography.

RESULTS: RBS supplementation improved serum triglyceride and free fatty acid levels in OVX rats. Histological analysis showed that RBS significantly attenuated hepatic fat accumulation and decreased hepatic lipid, total cholesterol, and triglyceride levels. Additionally, RBS suppressed the estrogen deficiency-induced upregulation of lipogenic genes, such as sterol regulatory element-binding protein 1 (SREBP1), acetyl-CoA carboxylase 1, fatty acid synthase, glycerol-3-phosphate acyltransferase, and stearoyl-CoA desaturase 1. CONCLUSIONS RBS and γ-oryzanol effectively reduced lipid accumulation in a HepG2 cell hepatic steatosis model. RBS improves OVX-induced hepatic steatosis by regulating the SREBP1-mediated activation of lipogenic genes, suggesting the benefits of RBS in preventing fatty liver in postmenopausal women.