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Since the foot-and-mouth disease (FMD) outbreak in South Korea in 2010–2011, vaccination policies utilizing inactivated FMD vaccines composed of types O and A have been implemented nationwide. However, because type Asia1 occurred in North Korea in 2007 and intermittently in neighboring countries, the risk of type Asia1 introduction cannot be ruled out. This study evaluated the antigen yield and viral inactivation kinetics of the recombinant Asia1 Shamir vaccine strain (Asia1 Shamir-R). When Asia1 Shamir-R was proliferated in shaking flasks (1 L), a 2 L bioreactor (1 L), and a wave bioreactor (25 L), the antigen yields were 7.5 μg/mL, 5.2 μg/mL, and 3.8 μg/mL, respectively. The optimal FMDV inactivation conditions were 2 mM BEI at 26 °C and 1.0 mM BEI at 37 °C. There was no antigen loss due to BEI treatment, and only a decrease in antigen levels was observed during storage. The sera from pigs immunized with antigen derived from a bioreactor exhibited a neutralizing antibody titer of approximately 1/1000 against Asia1 Shamir and Asia1/MOG/05 viruses; therefore, Asia1 Shamir-R is expected to provide sufficient protection against both viruses. If an FMD vaccine production facility is established, this Asia1 Shamir-R can be employed for domestic antigen banks in South Korea. [ABSTRACT FROM AUTHOR]

South Korea has experienced outbreaks of foot-and-mouth disease (FMD) of serotypes O and A, leading to nationwide vaccination with a bivalent vaccine. Since the FMD virus (FMDV) Asia1 group-V genotype occurred in North Korea in 2007, an Asia1/MOG/05 vaccine strain belonging to the Asia1 group-V genotype was developed using a genetic recombination method (Asia1/MOG/05-R). This study aimed to evaluate the antigen productivity and viral inactivation kinetics of Asia1/MOG/05-R to assess its commercial viability. The antigen yield of Asia1/MOG/05-R produced in flasks and bioreactors was approximately 4.0 μg/mL. Binary ethylenimine (BEI) inactivation kinetics of Asia1/MOG/05-R showed that 2 mM and 1.0 mM BEI treatment at 26 °C and 37 °C, respectively, resulted in a virus titer <10−7 TCID50/mL within 24 h, meeting the inactivation kinetics criteria. During incubation at 26 °C and 37 °C, 10% antigen loss occurred, but not due to BEI treatment. When pigs were inoculated twice with the Asia1/MOG/05-R antigen, the virus neutralization titer increased to approximately 1:1000; therefore, it can sufficiently protect against Asia1/MOG/05-R and Asia1 Shamir viruses. The Asia1/MOG/05-R will be useful as a vaccine strain for domestic antigen banks. [ABSTRACT FROM AUTHOR]

Foot-and-mouth disease (FMD) is a highly infectious disease affecting cloven-hoofed animals and causes significant economic losses to the livestock industry. The Type O PanAsia-2 (O PA-2) vaccine strain is protective against a wide range of serotype O FMD virus (FMDV) strains in East Asia, and A22 Iraq/24/64 (A22 IRQ) is the most widely used vaccine strain in FMD vaccine antigen banks. The aim of this study was to produce antigens from O PA-2 and A22 IRQ viruses using a 100 L bioreactor and evaluate the protective efficacy of varying antigen concentrations in pigs. More than 2 μg/mL of the antigen was recovered from the O PA-2 and A22 IRQ virus-infected supernatants. Further, inactivation of O PA-2 and A22 IRQ by binary ethyleneimine revealed that the viral titers decreased below 10−7 TCID50/mL within 13 h and 9 h, respectively. The O PA-2 and A22 IRQ vaccines, containing 10 μg and 5 μg of antigen, respectively, provided protection against homologous viruses in pigs. This is the first report demonstrating that the antigens obtained from the pilot-scale production of O PA-2 and A22 IRQ are viable candidate vaccines. These results will pave the way for industrial-scale FMD vaccine production in South Korea. [ABSTRACT FROM AUTHOR]

Foot-and-mouth disease (FMD) vaccines must be produced in a biosafety level 3 facility, so the FMD virus (FMDV) must be completely inactivated after amplification. The inactivation kinetics of FMDV during vaccine antigen production were assessed by evaluating whether the viral titer dropped below 10−7 TCID50/mL within 24 h of binary ethyleneimine (BEI) treatment. This study dealt with four FMD vaccine candidate strains for the efficacy of BEI treatment at different concentrations and temperatures to determine the optimal inactivation condition of each virus. Two domestic isolates, O/SKR/Boeun/2017 (O BE) and A/SKR/Yeoncheon/2017 (A YC), and two recombinant viruses, PAK/44/2008 (O PA-2) and A22/Iraq/24/64 (A22 IRQ), were investigated. The O BE and A22 IRQ required 2 mM BEI at 26 °C and 0.5 mM BEI at 37 °C for complete inactivation. The O PA-2 and A YC required 2 mM BEI at 26 °C and 1 mM BEI at 37 °C. Crucially, the yield of FMD virus particles (146S) in the viral infection supernatant was higher (>4.0 µg/mL) than those previously reported; additionally, there was little antigen loss, even after 24 h of treatment with 3 mM BEI. Overall, it is considered economical to produce FMD vaccines using these four kinds of viruses; therefore, these candidate strains will be prioritized for the manufacture of FMD vaccines in South Korea. [ABSTRACT FROM AUTHOR]

Objectives Despite the preclinical evidence on protective effects of colchicine against kidney fibrosis, whether colchicine could delay the progression of chronic kidney disease (CKD) in humans remains unknown. This study examined the association between long-term colchicine use and risk of adverse kidney outcome in patients with CKD who were treated for hyperuricaemia or chronic gout. Methods We conducted a multicentre, nested, case-control study in three Korean hospitals. Patients were aged ≥19 years; had CKD G3–G4; and used drugs including colchicine, allopurinol and febuxostat for hyperuricaemia or chronic gout during the period from April 2000 to October 2020. Patients with CKD progression, which was defined as ≥40% decrease from the baseline estimated glomerular filtration rate or the onset of kidney failure with replacement therapy, were matched to controls based on follow-up time, age and sex. Results Overall, 3085 patients with CKD progression were matched to 11   715 control patients. Multivariate conditional logistic regression analysis showed that patients with ≥90 cumulative daily colchicine doses were associated with a lower risk of CKD progression [adjusted odds ratio (AOR), 0.77; 95% CI: 0.61, 0.96] than non-users. In the sensitivity analysis with matched CKD stages, the AOR was 0.77 (95% CI: 0.62, 0.97). This association was more pronounced in patients without diabetes or hypertension, and in patients with CKD G3. Conclusion Colchicine use is associated with a lower risk of adverse kidney outcomes in CKD patients with hyperuricaemia, or chronic gout. [ABSTRACT FROM AUTHOR]

The emission sources and their health risks of fine particulate matter (PM2.5) in Siheung, Republic of Korea, were investigated as a middle-sized industrial city. To identify the PM2.5 sources with error estimation, a positive matrix factorization model was conducted using daily mean speciated data from November 16, 2019, to October 2, 2020 (95 samples, 22 chemical species). As a result, 10 sources were identified: secondary nitrate (24.3%), secondary sulfate (18.8%), traffic (18.8%), combustion for heating (12.6%), biomass burning (11.8%), coal combustion (3.6%), heavy oil industry (1.8%), smelting industry (4.0%), sea salts (2.7%), and soil (1.7%). Based on the source apportionment results, health risks by inhalation of PM2.5 were assessed for each source using the concentration of toxic elements portioned. The estimated cumulative carcinogenic health risks from the coal combustion, heavy oil industry, and traffic sources exceeded the benchmark, 1E-06. Similarly, carcinogenic health risks from exposure to As and Cr exceeded 1E-05 and 1E-06, respectively, needing a risk reduction plan. The non-carcinogenic risk was smaller than the hazard index of one, implying low potential for adverse health effects. The probable locations of sources with relatively higher carcinogenic risks were tracked. In this study, health risk assessment was performed on the elements for which mass concentration and toxicity information were available; however, future research needs to reflect the toxicity of organic compounds, elemental carbon, and PM2.5 itself. [ABSTRACT FROM AUTHOR]

Gastric cancer is the fifth most common cancer type worldwide and one of the most frequently diagnosed cancers in South Korea. In this study, we propose DeepPrevention, which comprises a prediction module to predict the possibility of progression from atrophic gastritis to gastric cancer and an explanation module to identify risk factors for progression from atrophic gastritis to gastric cancer, to identify patients with atrophic gastritis who are at high risk of gastric cancer. The data set used in this study was South Korea National Health Insurance Service (NHIS) medical checkup data for atrophic gastritis patients from 2002 to 2013. Our experimental results showed that the most influential predictors of gastric cancer development were sex, smoking duration, and current smoking status. In addition, we found that the average age of gastric cancer diagnosis in a group of high-risk patients was 57, and income, BMI, regular exercise, and the number of endoscopic screenings did not show any significant difference between groups. At the individual level, we identified that there were relatively strong associations between gastric cancer and smoking duration and smoking status. [ABSTRACT FROM AUTHOR]

Background: Repeated inflammation of the pancreas can cause pancreatitis or diabetes. It is well recognized that the organic acidemias may be complicated by pancreatitis but less recognized are other metabolic disorders in which pancreatitis can occur. This study shows that long-term follow-up of patients with various metabolic disorders in Korea revealed several with episodes of isolated pancreatitis or diabetes concomitantly with pancreatitis.Results and Discussion: In this study, two patients with methylmalonic aciduria (MMA), two with propionic acidemia (PPA), one with fatty acid oxidation disorder (FAOD), and one with hyperornithinemia, gyrate atrophy, and juvenile onset diabetes mellitus (DM) were clinically followed for up to 10 - 21 years. Two Korean siblings with MMA showed recurrent pancreatitis from the age of 15 and 19, respectively. The frequency of admission due to pancreatitis was up to 11 times. One patient with MMA developed diabetes mellitus at the age of 20. The other patient with MMA developed recurrent pancreatitis at 4 years and diabetes at 8 years of age. One of the patients with PPA presented with diabetic ketoacidosis. The other PPA patient died of cardiac arrest at age 10. The patient with FAOD presented with pancreatitis at 10 years and died at the age of 15 years due to cardiac arrest. A 35-year-old woman with hyperornithinemia/gyrate atrophy was diagnosed with juvenile onset diabetes at the age of 7 years. No pancreatitis occurred during the follow-up period.Conclusions: We conclude that various metabolic disorders can trigger acute or chronic pancreatitis. Proper and prompt multidisciplinary management of metabolic derangement is crucial for preventing pancreatic damage. Further clinical and investigational studies are required to elucidate the pathogenesis of pancreatitis and diabetes mellitus in patients with inborn errors in metabolism. [ABSTRACT FROM AUTHOR]

The Republic of Korea is trying to reduce greenhouse gas emissions by 37% from business-as-usual levels by 2030. Reliable greenhouse gas inventory is prerequisite to making effective greenhouse gas reduction plans. Currently, Intergovernmental Panels on Climate Change default emission factors were used in biological treatment of the solid waste sector without any consideration of the biological treatment process in the Republic of Korea. In this study, greenhouse gas emissions from biological treatment facilities of food waste have been monitored in order to develop country-specific emission factors in the Republic of Korea. Greenhouse gas emissions were monitored in two composting facilities and one anaerobic digestion facility. All study sites possess a local exhaust ventilation system and odour treatment system. Continuous greenhouse gas monitoring has been conducted on gathered gases using a non-dispersive infrared detector before entering odour treatment systems. At composting facilities, the emission factors of CH4 and N2O were 0.17–0.19 g-CH4 kg-waste−1 and 0.10–0.13 g-N2O kg-waste−1, respectively. Especially, the emission factors of CH4 in composting facilities showed significantly low values compared with other countries owing to the air blowing by a pump at the studied sites. At anaerobic digestion facilities, the emission factors of CH4 and N2O were 1.03 g-CH4 kg-waste−1 and 0.53 g-N2O kg-waste−1. The emission factors estimated in this study showed a significant difference from the Intergovernmental Panels on Climate Change default value. Therefore, it is recommended to develop a country-specific emission factor in order to reflect the different processes of biological treatment of solid waste. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM 2.5) has been a pollutant of main interest globally for more than two decades, owing to its well-known adverse health effects. For developing effective management strategies for PM 2.5 , it is vital to identify its major sources and quantify how much they contribute to ambient PM 2.5 concentrations. With the expanded monitoring efforts established during recent decades in Korea, speciated PM 2.5 data needed for source apportionment of PM 2.5 are now available for multiple sites (cities). However, many cities in Korea still do not have any speciated PM 2.5 monitoring station, although quantification of source contributions for those cities is in great need. While there have been many PM 2.5 source apportionment studies throughout the world for several decades based on monitoring data collected from receptor site(s), none of those receptor-oriented studies could predict unobserved source contributions at unmonitored sites. This study predicts source contributions of PM 2.5 at unmonitored locations using a recently developed novel spatial multivariate receptor modeling (BSMRM) approach, which incorporates spatial correlation in data into modeling and estimation for spatial prediction of latent source contributions. The validity of BSMRM results is also assessed based on the data from a test site (city), not used in model development and estimation. [Display omitted] • Latent source contributions of PM 2.5 along with uncertainty were estimated by BSMRM. • BSMRM performed spatial prediction of source contributions at unmonitored sites. • BSMRM results were verified using held-out test data. • Maps of source-specific pollution surfaces over South Korea were constructed. [ABSTRACT FROM AUTHOR]

This study examines the relationships among distributive justice, procedural justice, and employee willingness to engage in customer-oriented behavior. Data collected from 328 employees at eight general hospitals in Korea show that distributive justice does and procedural justice does not directly affect employee willingness to engage in customer-oriented behavior. Procedural justice does, however, positively affect perceptions of distributive justice. [ABSTRACT FROM AUTHOR]

Accurate estimations of flood waste generation are a crucial issue in disaster waste management. Multilinear regression of related parameters has been recognized as a promising technique for flood waste estimation. There are two types of flood waste estimation methods: pre-event predictions using factors related to regional properties and rainfall hazards, and post-event predictions using damage variables due to floods, such as the number of damaged buildings. Previous attempts to establish these models used deterministic approaches; however, probabilistic methods have never been applied. Considering the large degrees of uncertainty in waste generation from floods, a probabilistic approach can provide a more accurate model compared to models developed by the conventional deterministic approach. This study applied Bayesian inference to develop a flood waste regression model in South Korea. The aims of the study are as follows: (1) to analyze the characteristics of coefficients estimated by the Bayesian approach; (2) evaluate the performance of the prediction model by Bayesian inference; and (3) assess the effectiveness of Bayesian updating in a flood waste estimation. According to the results, the coefficients obtained via Bayesian inference showed a more significant p -value compared to those developed through the deterministic approach. Bayesian inference with a null prior distribution was effective in error reduction, specifically for post-event prediction. Bayesian updating did not effectively increase the accuracy of the model, while iterative updating required a complex calculation process. These results reveal the potential of the Bayesian approach in flood waste estimations, which can be transferred to other countries. • Flood waste data contained large degree of uncertainty. • Bayesian approach was tried to resolve uncertainty in flood waste estimation. • Bayesian regression reduces error in post-event flood waste estimation. • Suggested framework can be transferred to international context. [ABSTRACT FROM AUTHOR]

• Flood waste data contained case-dependent heterogeneity. • Multiple regression of damage variables was performed for flood waste estimation. • Data grouping may enhance the accuracy of flood waste estimation. • Sequential grouping may result in more efficient flood waste prediction. Flood waste management is important for reducing the damage and secondary environmental pollution caused by delays in disaster recovery. One key issue related to flood waste management concerns estimating the precise quantity of waste to plan recovery strategies and policies. In this study, an advanced flood waste estimation technique was devised using data stratification. In total 90 flood cases in South Korea were sorted by three strata characteristics: administrative region (AR; equivalent to special city or province), urbanization rate (UR), and disaster type and coastal accessibility (DC). According to the results, such data stratification led to flood waste prediction improvement not only by the single-stage stratification but also by successive stratifications. Data stratification was effective both for identifying groups with similar contexts and for eliminating disparities in the dataset that impede accurate waste prediction. Among the stratification sequences tested, the order resulted in the most improvement in flood waste prediction was UR, AR, and DC. This stratification order yielded enhanced waste prediction in 74 cases. Since this study deals with a strategy to resolve gaps in disaster data, which is a crucial issue in many countries, it is envisaged that this strategy can be transferred to other countries. [ABSTRACT FROM AUTHOR]

The concentrations of fine particulate matter (PM 2.5) constituents, which are very important and essential information for the identification of air pollution sources, were predicted at three sites (Seoul, Ulsan, Baengnyeong) in South Korea between 2016 and 2018 using four machine learning (ML) models: generative adversarial imputation network (GAIN), fully connected deep neural network (FCDNN), random forest (RF), and k-nearest neighbor (kNN). 3 PM 2.5 constituent groups, namely 8 ions, 2 carbons, and 15 trace elements, were targeted for prediction. The latest hyperparameter optimization techniques were used to learn air pollution characteristics from ambient PM 2.5 -related information, such as time, meteorology, and air pollutant concentrations. We compared the feature extraction abilities of the four models. The prediction accuracy identified by the coefficient of determination (R2) between prediction and observation was highest in GAIN, followed by FCDNN and RF or kNN. On availability of data on the time, air pollutant concentrations, and/or meteorology, simultaneously missed 20 % data of all PM 2.5 constituent groups were predicted, with R2 = 0.897, 0.861, 0.785, and 0.801 by the GAIN, FCDNN, RF, and kNN, respectively. As missing ratios (20 %, 40 %, 60 %, 80 %) of input data increased, prediction accuracy decreased in the four models and was predominantly more noticeable in GAIN and kNN. As the available period of data increased, prediction accuracy increased in GAIN and FCDNN. Trace elements were predicted with the lowest R2 in all models among the target constituent groups. Study sites with more emission sources showed lower prediction accuracy, resulting in the highest R2 in Baengnyeong island and the lowest in Ulsan. According to the current findings, ML models can be used to evaluate various air pollution issues for which data is missing. [ABSTRACT FROM AUTHOR]