Background/Aims: It remains unclear which maintenance treatment modality is most appropriate for mild gastroesophageal reflux disease (GERD).We aimed to compare on-demand treatment with continuous treatment using a proton pump inhibitor (PPI) in the maintenance treatment for patients with non-erosive GERD or mild erosive esophagitis. Methods: Patients whose GERD symptoms improved after 4 weeks of standard dose PPI treatment were prospectively enrolled at 25 hospitals.Subsequently, the enrolled patients were randomly assigned to either an on-demand or a continuous maintenance treatment group, and followed in an 8-week interval for up to 24 weeks. Results: A total of 304 patients were randomized to maintenance treatment (continuous, n = 151 vs on-demand, n = 153). The primary outcome, the overall proportion of unwillingness to continue the assigned maintenance treatment modality, failed to confirm the noninferiority of on-demand treatment (45.9%) compared to continuous treatment (36.1%). Compared with the on-demand group, the GERD symptom and health-related quality of life scores significantly more improved and the overall satisfaction score was significantly higher in the continuous treatment group, particularly at week 8 and week 16 of maintenance treatment. Work impairment scores were not different in the 2 groups, but the prescription cost was less in the on-demand group. Serum gastrin levels significantly elevated in the continuous treatment group, but not in the on-demand group. Conclusions Continuous treatment seems to be more appropriate for the initial maintenance treatment of non-erosive GERD or mild erosive esophagitis than on-demand treatment. Stepping down to on-demand treatment needs to be considered after a sufficient period of continuous treatment.

Objective: This study was carried out to establish a basis for understanding and correcting the problems of the emergency physicians (EP) in Korea by examining the current status through the 2020 emergency physician survey conducted by the Korean Society of Emergency Medicine (KSEM). Methods: An online survey was conducted from January 15 to February 26, 2021, through a website and mobile application. The final questionnaire was classified into three types based on the clinical work in the emergency department (ED), current position, and the year of board certification. The contents of the questionnaire were organized into eight themes, and a subcommittee was put in charge of each. Results: Of the 400 emergency medical institutions surveyed, 295 hospitals (74.2%) had board-certified EPs, and 1,453 EPs (72.5%) out of 2,000 were working in the ED. Of these, on average, 7.2 EPs worked together, and they worked 11.6 days/153.1 hours per month and did night shifts 5.9 days per month. 30.7% showed excessive sleepiness during the day, and only 9.6% said that they could adhere to mealtimes. 27.1% responded that they suffered from depression. The level of satisfaction was 3.2 points, and 43.0% responded that they would choose emergency medicine (EM) again as their major. The most important factor in choosing EM was the availability of adequate free time. Their responses indicated that the retirement age was 59.7 years, and the upper age limit for the night shift was 54.3 years. Conclusion Though there have been improvements since the first survey in 2010, there was no notable increase in EP satisfaction. Therefore, appropriate future policies should be formulated and implemented based on these survey results.

Background: As the number of large-scale studies involving multiple organizations producing data has steadily increased, an integrated system for a common interoperable format is needed. In response to the coronavirus disease 2019 (COVID-19) pandemic, a number of global efforts are underway to develop vaccines and therapeutics. We are therefore observing an explosion in the proliferation of COVID-19 data, and interoperability is highly requested in multiple institutions participating simultaneously in COVID-19 pandemic research. Results: In this study, a laboratory information management system (LIMS) approach has been adopted to systemically manage various COVID-19 non-clinical trial data, including mortality, clinical signs, body weight, body temperature, organ weights, viral titer (viral replication and viral RNA), and multiorgan histopathology, from multiple institutions based on a web interface. The main aim of the implemented system is to integrate, standardize, and organize data collected from laboratories in multiple institutes for COVID-19 non-clinical efficacy testings. Six animal biosafety level 3 institutions proved the feasibility of our system. Substantial benefits were shown by maximizing collaborative high-quality non-clinical research. Conclusions This LIMS platform can be used for future outbreaks, leading to accelerated medical product development through the systematic management of extensive data from non-clinical animal studies.

Background/Aims: Recurrent acute myocardial infarction (AMI) is an adverse cardiac event in patients with a first AMI. The predictors of recurrent AMI after the first AMI in patients who underwent successful percutaneous coronary intervention (PCI) have not been elucidated. Methods: We analyzed the data collected from 9,869 patients (63.2 ± 12.4 years, men:women = 7,446:2,423) who were enrolled in the Korea Acute Myocardial Infarction Registry-National Institute of Health between November 2011 and October 2015, had suffered their first AMI and had received successful PCI during the index hospitalization. Multivariable logistic regression analysis was performed to identify the independent predictors of recurrent AMI following the first AMI. Results: The cumulative incidence of recurrent AMI after successful PCI was 3.6% (359/9,869). According to the multivariable logistic regression analysis, the significant predictive factors for recurrent AMI were diabetes mellitus, renal dysfunction, atypical chest pain, and multivessel disease. Conclusions In this Korean prospective cohort study, the independent predictors of recurrent AMI after successful PCI for the first AMI were diabetes mellitus, renal dysfunction, atypical chest pain, and multivessel disease.

Unilateral pulmonary vein atresia (PVA) is a rare congenital cardiovascular anomaly occurring after the common pulmonary vein fails to incorporate into the left atrium. It is most commonly diagnosed in childhood, and diagnosis after reaching adulthood is extremely rare. Dyspnea on exertion and hemoptysis are common clinical features in adult PVA patients, whereas lung parenchymal abnormalities are indirect signs of PVA, which can manifest as interstitial lung disease. Herein, we present the case of a 62-year-old female diagnosed with unilateral PVA presenting as unilateral interstitial lung disease and report the changes in her chest radiographs over 12 years.

Tamoxifen (TAM) is an anticancer drug used to treat estrogen receptor (ER)‒positive breast cancer. However, its ER-independent cytotoxic and antifungal activities have prompted debates on its mechanism of action. To achieve a better understanding of the ER-independent antifungal action mechanisms of TAM, we systematically identified TAM-sensitive genes through microarray screening of the heterozygous gene deletion library in fission yeast (Schizosaccharomyces pombe). Secondary confirmation was followed by a spotting assay, finally yielding 13 TAM-sensitive genes under the drug-induced haploinsufficient condition. For these 13 TAM-sensitive genes, we conducted a comparative analysis of their Gene Ontology (GO) ‘biological process’ terms identified from other genome-wide screenings of the budding yeast deletion library and the MCF7breast cancer cell line. Several TAM-sensitive genes overlapped between the yeast strains and MCF7 in GO terms including ‘cell cycle’ (cdc2, rik1, pas1, and leo1), ‘signaling’ (sck2, oga1, and cki3), and ‘vesicle-mediated transport’ (SPCC126.08c, vps54, sec72, and tvp15), suggesting their roles in the ER-independent cytotoxic effects of TAM. We recently reported that the cki3 gene with the ‘signaling’ GO term was related to the ER-independent antifungal action mechanisms of TAM in yeast. In this study, we report that haploinsufficiency of the essential vps54 gene, which encodes the GARP complex subunit, significantly aggravated TAM sensitivity and led to an enlarged vesicle structure in comparison with the SP286 control strain. These results strongly suggest that the vesicle-mediated transport process might be another action mechanism of the ER-independent antifungal or cytotoxic effects of TAM.

Decision-making for treatment of newly diagnosed prostate cancer (PCa) is complex due to the multiple initial treatment modalities available. We aimed to externally validate the SCaP (Severance Study Group of Prostate Cancer) Survival Calculator that incorporates a long short-term memory artificial neural network (ANN) model to estimate survival outcomes of PCa according to initial treatment modality. Materials and Methods The validation cohort consisted of clinicopathological data of 4,415 patients diagnosed with biopsy-proven PCa between April 2005 and November 2018 at three institutions. Area under the curves (AUCs) and time-to-event calibration plots were utilized to determine the predictive accuracies of the SCaP Survival Calculator in terms of progression to castration-resistant PCa (CRPC)–free survival, cancer-specific survival (CSS), and overall survival (OS). Results Excellent discrimination was observed for CRPC-free survival, CSS, and OS outcomes, with AUCs of 0.962, 0.944, and 0.884 for 5-year outcomes and 0.959, 0.928, and 0.854 for 10-year outcomes, respectively. The AUC values were higher for all survival endpoints compared to those of the development cohort. Calibration plots showed that predicted probabilities of 5-year survival endpoints had concordance comparable to those of the observed frequencies. However, calibration performances declined for 10-year predictions with an overall underestimation. Conclusion The SCaP Survival Calculator is a reliable and useful tool for determining the optimal initial treatment modality and for guiding survival predictions for patients with newly diagnosed PCa. Further modifications in the ANN model incorporating cases with more extended follow-up periods are warranted to improve the ANN model for long-term predictions.

Decision-making for treatment of newly diagnosed prostate cancer (PCa) is complex due to the multiple initial treatment modalities available. We aimed to externally validate the SCaP (Severance Study Group of Prostate Cancer) Survival Calculator that incorporates a long short-term memory artificial neural network (ANN) model to estimate survival outcomes of PCa according to initial treatment modality. Materials and Methods The validation cohort consisted of clinicopathological data of 4,415 patients diagnosed with biopsy-proven PCa between April 2005 and November 2018 at three institutions. Area under the curves (AUCs) and time-to-event calibration plots were utilized to determine the predictive accuracies of the SCaP Survival Calculator in terms of progression to castration-resistant PCa (CRPC)–free survival, cancer-specific survival (CSS), and overall survival (OS). Results Excellent discrimination was observed for CRPC-free survival, CSS, and OS outcomes, with AUCs of 0.962, 0.944, and 0.884 for 5-year outcomes and 0.959, 0.928, and 0.854 for 10-year outcomes, respectively. The AUC values were higher for all survival endpoints compared to those of the development cohort. Calibration plots showed that predicted probabilities of 5-year survival endpoints had concordance comparable to those of the observed frequencies. However, calibration performances declined for 10-year predictions with an overall underestimation. Conclusion The SCaP Survival Calculator is a reliable and useful tool for determining the optimal initial treatment modality and for guiding survival predictions for patients with newly diagnosed PCa. Further modifications in the ANN model incorporating cases with more extended follow-up periods are warranted to improve the ANN model for long-term predictions.