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: Diabetes mellitus (DM) is one of the most common endocrine diseases, and the relationship between diabetes and skin diseases is well-known and its mechanisms have been studied. Objective: This study aimed to examine the association between DM and skin diseases. Methods: We used the medical record database provided by the National Health Insurance Service. We constructed a cohort with 1,197,225 patients diagnosed with type 2 DM from 2011 to 2015. We analyzed 3,992,368 medical records of patients with DM who visited the hospital from January 1, 2009 to December 13, 2018 with skin and subcutaneous tissue diseases (ICD-10 code, L00-L99). After that, we compared the changes in skin and subcutaneous tissue diseases before and after the diagnosis of type 2 DM. Results: The number of patients with skin diseases, after the diagnosis of type 2 DM was 1,629,756 (50.6%). The frequency of skin diseases increased after the diagnosis of type 2 DM compared to that before the diagnosis. Particularly, infectious diseases (＋29.03%,p＜0.001), vesiculobullous diseases (＋33.13%, p＜0.001) and ulcerrelated diseases (pressure sores [＋530.18%], and lower extremity ulcers [＋321.56%], p＜0.001) increased sharply whereas dermatitis and eczematous diseases (−9.96%, p＜0.001) and urticaria (−12.99%, p＜0.001) decreased. Conclusion Skin diseases increased following the diagnosis of diabetes, and there were changes in the pattern of skin diseases before and after the diagnosis of diabetes.

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.

The KOrean College of Rheumatology BIOlogics and targeted therapy (KOBIO) registry is a nationwide observational cohort that captures detailed data on exposure of patients to biologic and targeted synthetic disease-modifying anti-rheumatic drugs (DMARDs). This registry was launched in December 2012 with an aim to prospectively investigate clinical manifestations and outcomes of patients with rheumatoid arthritis (RA), ankylosing spondylitis, and psoriatic arthritis who initiated a biologic or targeted synthetic DMARD or switched to another. Demographic data, disease activity, current treatment, adverse events, terms based on Medical Dictionary for Regulatory Activities, and so on are registered for patients who are then followed up annually in a web-based unified platform. The KOBIO registry also recruits and collects data of patients with RA on conventional DMARDs for comparison. As of today, more than 5,500 patients were enrolled from 47 academic and community Rheumatology centers across Korea. The KOBIO registry has evolved to become a powerful database for clinical research to improve clinical outcomes and quality of treatment.

The KOrean College of Rheumatology BIOlogics and targeted therapy (KOBIO) registry is a nationwide observational cohort that captures detailed data on exposure of patients to biologic and targeted synthetic disease-modifying anti-rheumatic drugs (DMARDs). This registry was launched in December 2012 with an aim to prospectively investigate clinical manifestations and outcomes of patients with rheumatoid arthritis (RA), ankylosing spondylitis, and psoriatic arthritis who initiated a biologic or targeted synthetic DMARD or switched to another. Demographic data, disease activity, current treatment, adverse events, terms based on Medical Dictionary for Regulatory Activities, and so on are registered for patients who are then followed up annually in a web-based unified platform. The KOBIO registry also recruits and collects data of patients with RA on conventional DMARDs for comparison. As of today, more than 5,500 patients were enrolled from 47 academic and community Rheumatology centers across Korea. The KOBIO registry has evolved to become a powerful database for clinical research to improve clinical outcomes and quality of treatment.

BACKGROUND/OBJECTIVES: Premenstrual syndrome (PMS) is a disorder characterized by repeated emotional, behavioral, and physical symptoms before menstruation, and the exact cause and mechanism are uncertain. Hyperprolactinemia interferes with the normal production of estrogen and progesterone, leading to PMS symptoms. Thus, we judged that the inhibition of prolactin hypersecretion could mitigate PMS symptoms.MATERIALS/METHODS: Hordeum vulgare L. extract (HVE), Chrysanthemum zawadskii var. latilobum extract (CZE), and Lomens-P0 the mixture of these extracts were tested in subsequent experiments. The effect of extracts on prolactin secretion at the in vitro level was measured in GH3 cells. Nitric oxide and pro-inflammatory mediator expression were measured in RAW 264.7 cells to confirm the anti-inflammatory effect. Also, the hyperprolactinemic Institute for Cancer Research (ICR) mice model was used to measure extract effects on prolactin and hormone secretion and uterine inflammation. RESULTS: Anti-inflammatory effects of and prolactin secretion suppress by HVE and CZE were confirmed through in vitro experiments (P < 0.05). Treatment with Lomens-P0 inhibited prolactin secretion (P < 0.05) and restored normal sex hormone secretion in the hyperprolactinemia mice model. In addition, extracts significantly inhibited the expression of pro-inflammatory biomarkers, including interleukin-1β, and -6, tumor necrosis factor-α, inducible nitric oxide synthase, and cyclooxygenase-2 (P < 0.01). We used high-performance liquid chromatography analyses to identify tricin and chlorogenic acid as the respective components of HVE and CZE that inhibit prolactin secretion. The Lomens-P0, which includes tricin and chlorogenic acid, is expected to be effective in improving PMS symptoms in the human body. CONCLUSIONS The Lomens-P0 suppressed the prolactin secretion in hyperprolactinemia mice, normalized the sex hormone imbalance, and significantly suppressed the expression of inflammatory markers in uterine tissue. This study suggests that Lomens-P0 may have the potential to prevent or remedy materials to PMS symptoms.

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.