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.

Vitamin C is a well-known antioxidant with antiviral, anticancer, and anti-inflammatory properties based on its antioxidative function. Aptamin C, a complex of vitamin C with its specific aptamer, has been reported to maintain or even enhance the efficacy of vitamin C while increasing its stability. To investigate in vivo distribution of Aptamin C, Gulo knockout mice, which, like humans, cannot biosynthesize vitamin C, were administered Aptamin C orally for 2 and 4 weeks.The results showed higher vitamin C accumulation in all tissues when administered Aptamin C, especially in the spleen. Next, the activity of natural killer (NK) cells were conducted. CD69, a marker known for activating for NK cells, which had decreased due to vitamin C deficiency, did not recover with vitamin C treatment but showed an increasing with Aptamin C. Furthermore, the expression of CD107a, a cell surface marker that increases during the killing process of target cells, also did not recover with vitamin C but increased with Aptamin C. Based on these results, when cultured with tumor cells to measure the extent of tumor cell death, an increase in tumor cell death was observed. To investigate the signaling mechanisms and related molecules involved in the proliferation and activation of NK cells by Aptamin C showed that Aptamin C treatment led to an increase in intracellular STAT3 activation. In conclusion, Aptamin C has a higher capability to activate NK cells and induce tumor cell death compared to vitamin C and it is mediated through the activation of STAT3.

Purpose: High-dose radiotherapy (RT) for localized prostate cancer requires careful consideration of target position changes and adjacent organs-at-risk (OARs), such as the rectum and bladder. Therefore, daily monitoring of target position and OAR changes is crucial in minimizing interfractional dosimetric uncertainties. For efficient monitoring of the internal condition of patients, we assessed the feasibility of an auto-segmentation of OARs on the daily acquired images, such as megavoltage computed tomography (MVCT), via a commercial artificial intelligence (AI)-based solution in this study. Materials and Methods: We collected MVCT images weekly during the entire course of RT for 100 prostate cancer patients treated with the helical TomoTherapy system. Based on the manually contoured body outline, the bladder including prostate area, and rectal balloon regions for the 100 MVCT images, we trained the commercially available fully convolutional (FC)-DenseNet model and tested its auto-contouring performance. Results: Based on the optimally determined hyperparameters, the FC-DenseNet model successfully auto-contoured all regions of interest showing high dice similarity coefficient (DSC) over 0.8 and a small mean surface distance (MSD) within 1.43 mm in reference to the manually contoured data. With this well-trained AI model, we have efficiently monitored the patient's internal condition through six MVCT scans, analyzing DSC, MSD, centroid, and volume differences. Conclusion We have verified the feasibility of utilizing a commercial AI-based model for auto-segmentation with low-quality daily MVCT images. In the future, we will establish a fast and accurate auto-segmentation and internal organ monitoring system for efficiently determining the time for adaptive replanning.

Purpose: This descriptive study aimed to identify the incidence and related factors of reintubation after unplanned extubation in the neonatal intensive care unit. Methods: A secondary data analysis was conducted using electronic medical records. All events of unplanned extubation were audited from January 2020 to August 2021. The data were analyzed by chi-square test using IBM SPSS Statistics ver. 24.0 program. Results: Fifty-eight unplanned extubation events were identified for 20 months. The incidence was 2.6 per 100 ventilation days during the study period. After unplanned extubation, 35 neonates (60.3%) were immediately reintubated. There was a statistically significant difference between the gestational age (p=0.018) and postconceptional age at unplanned extubation (p=0.044) and the total intubation period (p=0.003) between the reintubation and nonreintubation groups. Conclusion These findings indicate that the incidence of unplanned extubation was significantly higher than that of an adult in South Korea. According to this study, targeting interventions are required to prevent unplanned extubation and ensure patient safety.

Purpose: This study is qualitative study using phenomenology approach of Colazzi, to identify meaning and essential structure of the hospitalization with Complex Regional Pain Syndrome (CRPS). Methods: Experiential data were collected through in-depth interviews with 10 patients who had been hospitalized in hospital rehabilitation medical wards. The main question was ‘Could you describe your hospitalization experience with CRPS? Results: Five categories obtained were ‘Experience despair in the swamp of long suffering’, ‘A hospital system that only adherence to treatment procedures’, ‘There are aggravating factors all over the hospital environment’, ‘Finding support to cover the disease’, and ‘Cross the tunnel of pain with the medical staff’. Conclusion Patients with complex pain syndrome experienced extreme pain and sudden pain even after hospitalization, and were more aggravated due to treatment procedures and regulation systems. It is thought that multidisciplinary team approach nursing intervention is necessary to improve this situation.

Background: Occupational cancer is a global health issue. The Korean CARcinogen EXposure (K-CAREX), a database of CARcinogen EXposure, was developed for the Korean labor force to estimate the number of workers exposed to carcinogens by industry. The present study aimed to estimate the intensity of exposure to carcinogens by industry, in order to supply complementary information about CARcinogen EXposure intensity to the K-CAREX. Methods: We used nationwide workplace monitoring data from 2014 to 2016 and selected target carcinogens based on the K-CAREX list. We computed the 95th percentile levels of measurements for each industry by carcinogens. Based on the 95th percentile level relative to the occupational exposure limit, we classified the CARcinogen EXposure intensity into five exposure ratings (1–5) for each industry. Results: The exposure ratings were estimated for 21 carcinogenic agents in each of the 228 minor industry groups. For example, 3,058 samples were measured for benzene in the manufacturing industry of basic chemicals. This industry was assigned a benzene exposure rating of 3. Conclusions We evaluated the CARcinogen EXposure ratings across industries in Korean workers. The results will provide information on the exposure intensity to carcinogens for integration into the K-CAREX. Furthermore, it will aid in prioritizing control efforts and identifying industries of concern.

Mutations in the Leucine-rich repeat kinase 2 (LRRK2 ) gene are the most prevalent cause of familial Parkinson’s disease (PD). The increase in LRRK2 kinase activity observed in the pathogenic G2019S mutation is important for PD development. Several studies have reported that increased LRRK2 kinase activity and treatment with LRRK2 kinase inhibitors decreased and increased ciliogenesis, respectively, in mouse embryonic fibroblasts (MEFs) and retinal pigment epithelium (RPE) cells. In contrast, treatment of SH-SY5Y dopaminergic neuronal cells with PD-causing chemicals increased ciliogenesis. Because these reports were somewhat contradictory, we tested the effect of LRRK2 kinase activity on ciliogenesis in neurons. In SH-SY5Y cells, LRRK2 inhibitor treatment slightly increased ciliogenesis, but serum starvation showed no increase. In rat primary neurons, LRRK2 inhibitor treatment repeatedly showed no significant change. Little difference was observed between primary cortical neurons prepared from wild-type (WT) and G2019S +/- mice. However, a significant increase in ciliogenesis was observed in G2019S +/- compared to WT human fibroblasts, and this pattern was maintained in neural stem cells (NSCs) differentiated from the induced pluripotent stem cells (iPSCs) prepared from the same WT/G2019S fibroblast pair. NSCs differentiated from G2019S and its gene-corrected WT counterpart iPSCs were also used to test ciliogenesis in an isogenic background. The results showed no significant difference between WT and G2019S regardless of kinase inhibitor treatment and B27-deprivation-mimicking serum starvation. These results suggest that LRRK2 kinase activity may be not a direct regulator of ciliogenesis and ciliogenesis varies depending upon the cell type or genetic background.