Background: The stimulatory effects of caffeine contribute to poor sleep quality. However, the relationship between caffeinated beverages and sleep quality, considering frequency or types of caffeinated beverages, were not extensively studied. Methods: Data were collected from 160 urban workers (75 men [46.9%] aged 20–69 years; with an average age of 41.8±12.3 years) using a structured, self-administered online questionnaire. Sleep quality, time, satisfaction; types and frequency of caffeinated beverages (number of cups per week; Q1: 0 cup, Q4: 14 or more cups per week), demographics, and health behaviors were asked. Sleep quality were evaluated using the Korean version of the Pittsburgh Sleep Quality Index (PSQI-K). Multiple regression analysis was conducted on the association between the frequency of caffeinated beverages consumption and sleep quality. Results: The most frequently consumed beverages were unsweetened coffee (22.8%) and the most common time for caffeine was between 12 pm to 5 pm (58.2%). The average sleep quality score based on the PSQI-K was 6.0±2.0 overall, 5.3±1.6 in Q1, and 6.6±2.2 in Q4 (frequent caffeinated beverage drinkers), indicating poorer sleep quality in Q4 (P=0.022). In Q1, 13.3% rated their sleep quality as ‘very good,’ while in Q4, only 2.5% gave the same rating. Poor sleep quality was significantly associated with the frequency of caffeinated beverages per week (β=0.232, P=0.004) and self-reported stress level (β=0.256, P=0.002). Conclusions Frequent consumption of caffeinated beverages appears to be associated with poor sleep quality among urban workers. While reducing caffeine intake may contribute to improvements in sleep quality as a health promoting behavior, this hypothesis requires validation through future studies employing personalized intervention approaches.

Background: The stimulatory effects of caffeine contribute to poor sleep quality. However, the relationship between caffeinated beverages and sleep quality, considering frequency or types of caffeinated beverages, were not extensively studied. Methods: Data were collected from 160 urban workers (75 men [46.9%] aged 20–69 years; with an average age of 41.8±12.3 years) using a structured, self-administered online questionnaire. Sleep quality, time, satisfaction; types and frequency of caffeinated beverages (number of cups per week; Q1: 0 cup, Q4: 14 or more cups per week), demographics, and health behaviors were asked. Sleep quality were evaluated using the Korean version of the Pittsburgh Sleep Quality Index (PSQI-K). Multiple regression analysis was conducted on the association between the frequency of caffeinated beverages consumption and sleep quality. Results: The most frequently consumed beverages were unsweetened coffee (22.8%) and the most common time for caffeine was between 12 pm to 5 pm (58.2%). The average sleep quality score based on the PSQI-K was 6.0±2.0 overall, 5.3±1.6 in Q1, and 6.6±2.2 in Q4 (frequent caffeinated beverage drinkers), indicating poorer sleep quality in Q4 (P=0.022). In Q1, 13.3% rated their sleep quality as ‘very good,’ while in Q4, only 2.5% gave the same rating. Poor sleep quality was significantly associated with the frequency of caffeinated beverages per week (β=0.232, P=0.004) and self-reported stress level (β=0.256, P=0.002). Conclusions Frequent consumption of caffeinated beverages appears to be associated with poor sleep quality among urban workers. While reducing caffeine intake may contribute to improvements in sleep quality as a health promoting behavior, this hypothesis requires validation through future studies employing personalized intervention approaches.

Background: The stimulatory effects of caffeine contribute to poor sleep quality. However, the relationship between caffeinated beverages and sleep quality, considering frequency or types of caffeinated beverages, were not extensively studied. Methods: Data were collected from 160 urban workers (75 men [46.9%] aged 20–69 years; with an average age of 41.8±12.3 years) using a structured, self-administered online questionnaire. Sleep quality, time, satisfaction; types and frequency of caffeinated beverages (number of cups per week; Q1: 0 cup, Q4: 14 or more cups per week), demographics, and health behaviors were asked. Sleep quality were evaluated using the Korean version of the Pittsburgh Sleep Quality Index (PSQI-K). Multiple regression analysis was conducted on the association between the frequency of caffeinated beverages consumption and sleep quality. Results: The most frequently consumed beverages were unsweetened coffee (22.8%) and the most common time for caffeine was between 12 pm to 5 pm (58.2%). The average sleep quality score based on the PSQI-K was 6.0±2.0 overall, 5.3±1.6 in Q1, and 6.6±2.2 in Q4 (frequent caffeinated beverage drinkers), indicating poorer sleep quality in Q4 (P=0.022). In Q1, 13.3% rated their sleep quality as ‘very good,’ while in Q4, only 2.5% gave the same rating. Poor sleep quality was significantly associated with the frequency of caffeinated beverages per week (β=0.232, P=0.004) and self-reported stress level (β=0.256, P=0.002). Conclusions Frequent consumption of caffeinated beverages appears to be associated with poor sleep quality among urban workers. While reducing caffeine intake may contribute to improvements in sleep quality as a health promoting behavior, this hypothesis requires validation through future studies employing personalized intervention approaches.

Background: The stimulatory effects of caffeine contribute to poor sleep quality. However, the relationship between caffeinated beverages and sleep quality, considering frequency or types of caffeinated beverages, were not extensively studied. Methods: Data were collected from 160 urban workers (75 men [46.9%] aged 20–69 years; with an average age of 41.8±12.3 years) using a structured, self-administered online questionnaire. Sleep quality, time, satisfaction; types and frequency of caffeinated beverages (number of cups per week; Q1: 0 cup, Q4: 14 or more cups per week), demographics, and health behaviors were asked. Sleep quality were evaluated using the Korean version of the Pittsburgh Sleep Quality Index (PSQI-K). Multiple regression analysis was conducted on the association between the frequency of caffeinated beverages consumption and sleep quality. Results: The most frequently consumed beverages were unsweetened coffee (22.8%) and the most common time for caffeine was between 12 pm to 5 pm (58.2%). The average sleep quality score based on the PSQI-K was 6.0±2.0 overall, 5.3±1.6 in Q1, and 6.6±2.2 in Q4 (frequent caffeinated beverage drinkers), indicating poorer sleep quality in Q4 (P=0.022). In Q1, 13.3% rated their sleep quality as ‘very good,’ while in Q4, only 2.5% gave the same rating. Poor sleep quality was significantly associated with the frequency of caffeinated beverages per week (β=0.232, P=0.004) and self-reported stress level (β=0.256, P=0.002). Conclusions Frequent consumption of caffeinated beverages appears to be associated with poor sleep quality among urban workers. While reducing caffeine intake may contribute to improvements in sleep quality as a health promoting behavior, this hypothesis requires validation through future studies employing personalized intervention approaches.

To prevent the spread of the coronavirus disease 2019 (COVID-19) pandemic, vaccines have been authorized for emergency use and implemented worldwide. We present a case of de novo glomerulonephritis (GN) after use of the COVID-19 mRNA vaccine BNT162b2.A 48-year-old man with no relevant medical history was referred for sudden and persistent worsening of renal insufficiency 1.5 months after the second vaccine dose. He had arthralgia and skin rash a week after vaccination. Abdominal pain and diarrhea started 2 weeks later, and he was admitted to the hospital for enteritis treatment. Colonoscopy showed multiple ulcerations and petechiae suggestive of vasculitis in the terminal ileum. After prednisolone therapy, his gastrointestinal symptoms improved, but his renal function continued to deteriorate. Based on kidney biopsy findings and nephrotic-range proteinuria (5,306 mg/24 hours), he was diagnosed with anti-neutrophil cytoplasmic autoantibody (ANCA)-negative pauci-immune crescentic GN (CrGN). He received high-dose steroid pulse therapy and oral cyclophosphamide, and then, gradually underwent steroid tapering, with improvement in proteinuria and renal function over several weeks. Several cases of GN suspected to be related to COVID-19 vaccines have been reported. To our knowledge, this is the first case report of ANCA-negative pauci-immune crescentic CrGN with extrarenal involvement after COVID-19 mRNA vaccination. Our finding expands the spectrum of COVID-19 vaccine-associated GN.

Background: Liver fibrosis is an early stage of liver cirrhosis. As a reversible lesion before cirrhosis, liver failure, and liver cancer, it has been a target for drug discovery. Many antifibrotic candidates have shown promising results in experimental animal models; however, due to adverse clinical reactions, most antifibrotic agents are still preclinical. Therefore, rodent models have been used to examine the histopathological differences between the control and treatment groups to evaluate the efficacy of anti-fibrotic agents in non-clinical research. In addition, with improvements in digital image analysis incorporating artificial intelligence (AI), a few researchers have developed an automated quantification of fibrosis. However, the performance of multiple deep learning algorithms for the optimal quantification of hepatic fibrosis has not been evaluated. Here, we investigated three different localization algorithms, mask R-CNN, DeepLabV3+, and SSD, to detect hepatic fibrosis. Results: 5750 images with 7503 annotations were trained using the three algorithms, and the model performance was evaluated in large-scale images and compared to the training images. The results showed that the precision values were comparable among the algorithms. However, there was a gap in the recall, leading to a difference in model accuracy. The mask R-CNN outperformed the recall value (0.93) and showed the closest prediction results to the annotation for detecting hepatic fibrosis among the algorithms. DeepLabV3+ also showed good performance; however, it had limitations in the misprediction of hepatic fibrosis as inflammatory cells and connective tissue. The trained SSD showed the lowest performance and was limited in predicting hepatic fibrosis compared to the other algorithms because of its low recall value (0.75). Conclusions We suggest it would be a more useful tool to apply segmentation algorithms in implementing AI algorithms to predict hepatic fibrosis in non-clinical studies.

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.

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: To assess associations between surveillance intervals in a national hepatocellular carcinoma (HCC) surveillance program and receiving curative treatment and mortality using nationwide cohort data for Korea. Materials and Methods: Using the National Health Insurance Service Database of Korea, we retrospectively identified 3201852 patients, the target population of the national HCC surveillance program, between 2008 and 2017. After exclusion, a total of 64674 HCC patients were divided based on surveillance intervals: never screened, ≤6 months (6M), 7–12 months (1Y), 13–24 months (2Y), and 25–36 months (3Y). Associations for surveillance interval with the chance to receive curative therapy and all-cause mortality were analyzed. Results: The 6M group (51.9%) received curative therapy more often than the other groups (1Y, 48.3%; 2Y, 43.8%; 3Y, 41.3%; never screened, 34.5%). Odds ratio for receiving curative therapy among the other surveillance interval groups (1Y, 0.87; 2Y, 0.76; 3Y, 0.77;never screened, 0.57; p<0.001) were significantly lower than that of the 6M group. The hazard ratios (HRs) of all-cause mortality were 1.07, 1.14, and 1.37 for 2Y, 3Y, and never screened groups. The HR for the 1Y group (0.96; p=0.092) was not significantly different, and it was lower (0.91; p<0.001) than that of the 6M group after adjustment for lead-time bias. Curative therapy was associated with survival benefits (HR, 0.26; p<0.001). Conclusion HCC surveillance, especially at a surveillance interval of 6 months, increases the chance to receive curative therapy.

Purpose: To assess associations between surveillance intervals in a national hepatocellular carcinoma (HCC) surveillance program and receiving curative treatment and mortality using nationwide cohort data for Korea. Materials and Methods: Using the National Health Insurance Service Database of Korea, we retrospectively identified 3201852 patients, the target population of the national HCC surveillance program, between 2008 and 2017. After exclusion, a total of 64674 HCC patients were divided based on surveillance intervals: never screened, ≤6 months (6M), 7–12 months (1Y), 13–24 months (2Y), and 25–36 months (3Y). Associations for surveillance interval with the chance to receive curative therapy and all-cause mortality were analyzed. Results: The 6M group (51.9%) received curative therapy more often than the other groups (1Y, 48.3%; 2Y, 43.8%; 3Y, 41.3%; never screened, 34.5%). Odds ratio for receiving curative therapy among the other surveillance interval groups (1Y, 0.87; 2Y, 0.76; 3Y, 0.77;never screened, 0.57; p<0.001) were significantly lower than that of the 6M group. The hazard ratios (HRs) of all-cause mortality were 1.07, 1.14, and 1.37 for 2Y, 3Y, and never screened groups. The HR for the 1Y group (0.96; p=0.092) was not significantly different, and it was lower (0.91; p<0.001) than that of the 6M group after adjustment for lead-time bias. Curative therapy was associated with survival benefits (HR, 0.26; p<0.001). Conclusion HCC surveillance, especially at a surveillance interval of 6 months, increases the chance to receive curative therapy.