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Objectives: Estimating the number of deaths caused by smoking is crucial for developing and evaluating tobacco control and smoking cessation policies. This study aimed to determine smoking-attributable mortality (SAM) in Korea in 2020. Methods: Four large-scale cohorts from Korea were analyzed. A Cox proportional-hazards model was used to determine the hazard ratios (HRs) of smoking-related death. By conducting a meta-analysis of these HRs, the pooled HRs of smoking-related death for 41 diseases were estimated. Population-attributable fractions (PAFs) were calculated based on the smoking prevalence for 1995 in conjunction with the pooled HRs. Subsequently, SAM was derived using the PAF and the number of deaths recorded for each disease in 2020. Results: The pooled HR for all-cause mortality attributable to smoking was 1.73 for current men smokers (95% confidence interval [CI], 1.53 to 1.95) and 1.63 for current women smokers (95% CI, 1.37 to 1.94). Smoking accounted for 33.2% of all-cause deaths in men and 4.6% in women. Additionally, it was a factor in 71.8% of men lung cancer deaths and 11.9% of women lung cancer deaths. In 2020, smoking was responsible for 53 930 men deaths and 6283 women deaths, totaling 60 213 deaths. Conclusions: Cigarette smoking was responsible for a significant number of deaths in Korea in 2020. Monitoring the impact and societal burden of smoking is essential for effective tobacco control and harm prevention policies.

Abstract Background Nurses’ satisfaction has an impact on organizational and patient outcomes. Integrated care system in South Korea was established in 2015 to improve care quality and decrease caregiving burden. Since then, nurses’ satisfaction has increased due to an increase in nursing staffing. However, besides nurse staffing, various work environments still affect nurse satisfaction. Methods Individual online surveys were conducted with participants to determine their personal characteristics, work environments, and hospital characteristics. We used mixed-effects linear regression equation contained both fixed and random effects. Results This study included 2,913 nurses from 119 hospitals. Their average job satisfaction was less than 6 points out of 10 points. Age, shift type, perceived workload, and delegation criteria were significant factors influencing nurses’ satisfaction. There was no significant factor among hospital characteristics. The satisfaction level of nurses was high for no-night rotating shift, low perceived workload, and clear delegation criteria. Conclusions Nurses’ satisfaction is affected by several work environmental factors. Low nurse satisfaction has a substantial impact on both patients and nurses. Therefore, nurse managers and hospitals should determine factors influencing their satisfaction and develop strategies to improve their satisfaction.

IntroductionThis paper presents a comprehensive analysis of COVID-19 transmission dynamics using an infection network derived from epidemiological data in South Korea, covering the period from January 3, 2020, to July 11, 2021. The network illustrates infector-infectee relationships and provides invaluable insights for managing and mitigating the spread of the disease. However, significant missing data hinder conventional analysis of such networks from epidemiological surveillance.MethodsTo address this challenge, this article suggests a novel approach for categorizing individuals into four distinct groups, based on the classification of their infector or infectee status as either traced or untraced cases among all confirmed cases. The study analyzes the changes in the infection networks among untraced and traced cases across five distinct periods.ResultsThe four types of cases emphasize the impact of various factors, such as the implementation of public health strategies and the emergence of novel COVID-19 variants, which contribute to the propagation of COVID-19 transmission. One of the key findings is the identification of notable transmission patterns in specific age groups, particularly in those aged 20-29, 40-69, and 0-9, based on the four type classifications. Furthermore, we develop a novel real-time indicator to assess the potential for infectious disease transmission more effectively. By analyzing the lengths of connected components, this indicator facilitates improved predictions and enables policymakers to proactively respond, thereby helping to mitigate the effects of the pandemic on global communities.ConclusionThis study offers a novel approach to categorizing COVID-19 cases, provides insights into transmission patterns, and introduces a real-time indicator for better assessment and management of the disease transmission, thereby supporting more effective public health interventions.

IntroductionMitigating the spread of infectious diseases is of paramount concern for societal safety, necessitating the development of effective intervention measures. Epidemic simulation is widely used to evaluate the efficacy of such measures, but realistic simulation environments are crucial for meaningful insights. Despite the common use of contact-tracing data to construct realistic networks, they have inherent limitations. This study explores reconstructing simulation networks using link prediction methods as an alternative approach.MethodsThe primary objective of this study is to assess the effectiveness of intervention measures on the reconstructed network, focusing on the 2015 MERS-CoV outbreak in South Korea. Contact-tracing data were acquired, and simulation networks were reconstructed using the graph autoencoder (GAE)-based link prediction method. A scale-free (SF) network was employed for comparison purposes. Epidemic simulations were conducted to evaluate three intervention strategies: Mass Quarantine (MQ), Isolation, and Isolation combined with Acquaintance Quarantine (AQ + Isolation).ResultsSimulation results showed that AQ + Isolation was the most effective intervention on the GAE network, resulting in consistent epidemic curves due to high clustering coefficients. Conversely, MQ and AQ + Isolation were highly effective on the SF network, attributed to its low clustering coefficient and intervention sensitivity. Isolation alone exhibited reduced effectiveness. These findings emphasize the significant impact of network structure on intervention outcomes and suggest a potential overestimation of effectiveness in SF networks. Additionally, they highlight the complementary use of link prediction methods.DiscussionThis innovative methodology provides inspiration for enhancing simulation environments in future endeavors. It also offers valuable insights for informing public health decision-making processes, emphasizing the importance of realistic simulation environments and the potential of link prediction methods.

IntroductionQuantifying the transmissibility over time, particularly by region and age, using parameters such as serial interval and time-varying reproduction number, helps in formulating targeted interventions. Moreover, considering the impact of geographical factors on transmission provides valuable insights into the effectiveness of control measures.MethodsDrawing on a comprehensive dataset of COVID-19 cases in South Korea, we analyzed transmission dynamics with a focus on age and regional variations. The dataset, compiled through the efforts of dedicated epidemiologists, includes information on symptom onset dates, enabling detailed investigations. The pandemic was divided into distinct phases, aligning with changes in policies, emergence of variants, and vaccination efforts. We analyzed various interventions such as social distancing, vaccination rates, school closures, and population density. Key parameters like serial interval, heatmaps, and time-varying reproduction numbers were used to quantify age and region-specific transmission trends.ResultsAnalysis of transmission pairs within age groups highlighted the significant impact of school closure policies on the spread among individuals aged 0-19. This analysis also shed light on transmission dynamics within familial and educational settings. Changes in confirmed cases over time revealed a decrease in spread among individuals aged 65 and older, attributed to higher vaccination rates. Conversely, densely populated metropolitan areas experienced an increase in confirmed cases. Examination of time-varying reproduction numbers by region uncovered heterogeneity in transmission patterns, with regions implementing strict social distancing measures showing both increased confirmed cases and delayed spread, indicating the effectiveness of these policies.DiscussionOur findings underscore the importance of evaluating and tailoring epidemic control policies based on key COVID-19 parameters. The analysis of social distancing measures, school closures, and vaccine impact provides valuable insights into controlling transmission. By quantifying the impact of these interventions on different age groups and regions, we contribute to the ongoing efforts to combat the COVID-19 pandemic effectively.

Rationale & Objective: This study aimed to assess the effect of exposure to organic pollutants in adults with chronic kidney disease (CKD). Study Design: This was a cross-sectional and longitudinal analysis. Setting and Participants: Forty adults enrolled in the Chronic Renal Insufficiency Cohort (CRIC). Exposures: Exposure at baseline and longitudinally to various organic chemical pollutants. Outcomes: The outcomes were as follows: death; composite of congestive heart failure, myocardial infarction, and stroke; event-free survival from kidney failure or ≥50% decline in estimated glomerular filtration rate (eGFR); and longitudinal trajectory of eGFR. Analytical Approach: We used high-performance liquid chromatography with tandem mass spectrometry to measure urinary concentrations of bisphenols, phthalates, organophosphate pesticides, polycyclic aromatic hydrocarbons, melamine, and cyanuric acid at years 1, 3, and 5 after enrollment in the CRIC. Univariate and multivariable logistic regression were used to examine the association of individual compounds and classes of pollutants with the outcomes. The Cox proportional hazards model and Kaplan-Meier method were used to calculate hazard ratios and 95% CIs for each class of pollutants. Results: Median baseline eGFR and urinary protein-to-creatinine ratio were 33 mL/min/1.73 m2 and 0.58 mg/g, respectively. Of 52 compounds assayed, 30 were detectable in ≥50% of participants. Urinary chemical concentrations were comparable in patients with CKD and healthy individuals from contemporaneous National Health and Nutrition Examination Survey cohorts. Phthalates were the only class with a trend toward higher exposure in patients with CKD. There was an inverse relationship between exposure and the eGFR slopes for bisphenol F, mono-(3-carboxypropyl) phthalate, mono-benzyl phthalate, mono-[2-(carboxymethyl)hexyl] phthalate, and melamine. There were no associations between organic pollutant exposure and cardiovascular outcomes. Limitations: Small sample size, evaluation of single rather than combined exposures. Conclusions: Simultaneous measurement of multiple organic pollutants in adults with CKD is feasible. Exposure levels are comparable with healthy individuals. Select contaminants, especially in the phthalate class, may be associated with more rapid deterioration in kidney function. Plain-Language Summary: The effect of exposure to organic pollutants has not been studied in adults with chronic kidney disease. (CKD). To fill this gap, we measured the exposure to a wide range of chemicals that are found in plastics, personal care products, and food preparation. Overall, the exposure was similar to that noted in the healthy population living in the United States. Only select compounds, mainly phthalates, demonstrated a trend with a more rapid decline in kidney function. These findings provide a useful reference for future studies that aim to evaluate organic pollutant exposure in patients with CKD. This is significant because these exposures represent a modifiable risk factor for disease progression through alterations in diet or lifestyle.

The advection–dispersion equation has been widely used to analyze the intermediate field mixing of pollutants in natural streams. The dispersion coefficient, manipulating the dispersion term of the advection–dispersion equation, is a crucial parameter in predicting the transport distance and contaminated area in the water body. In this study, the transverse dispersion coefficient was estimated using machine learning regression methods applied to oversampled datasets. Previous research datasets used for this estimation were biased toward width-to-depth ratio (W/H) values ≤ 50, potentially leading to inaccuracies in estimating the transverse dispersion coefficient for datasets with W/H > 50. To address this issue, four oversampling techniques were employed to augment the dataset with W/H > 50, thereby mitigating the dataset’s imbalance. The estimation results obtained from data resampling with nonlinear regression method demonstrated improved prediction accuracy compared to the pre-oversampling results. Notably, the combination of adaptive synthetic sampling (ADASYN) and eXtreme Gradient Boosting regression (XGBoost) exhibited improved accuracy compared to other combinations of oversampling techniques and nonlinear regression methods. Through the combined ADASYN–XGBoost approach, it is possible to enhance the transverse dispersion coefficient estimation performance using only two variables, W/H and bed friction effects (U/U*), without adding channel sinuosity; this represents the effects of secondary currents.

Estimating key epidemiological parameters, such as incubation period, serial interval (SI), generation interval (GI) and latent period, is essential to quantify the transmissibility and effects of various interventions of COVID-19. These key parameters play a critical role in quantifying the basic reproduction number. With the hard work of epidemiological investigators in South Korea, estimating these key parameters has become possible based on infector-infectee surveillance data of COVID-19 between February 2020 and April 2021. Herein, the mean incubation period was estimated to be 4.9 days (95% CI: 4.2, 5.7) and the mean generation interval was estimated to be 4.3 days (95% CI: 4.2, 4.4). The mean serial interval was estimated to be 4.3, with a standard deviation of 4.2. It is also revealed that the proportion of presymptomatic transmission was ~57%, which indicates the potential risk of transmission before the disease onset. We compared the time-varying reproduction number based on GI and SI and found that the time-varying reproduction number based on GI may result in a larger estimation of Rt, which refers to the COVID-19 transmission potential around the rapid increase of cases. This highlights the importance of considering presymptomatic transmission and generation intervals when estimating the time-varying reproduction number.

Motivated by regulating/eliminating the population of herbivorous pests, we investigate a discrete-time plant–herbivore model with two different constant control strategies (removal versus reduction), and formulate the corresponding optimal control problems when its dynamics exhibits varied types of bi-stability and fluctuating environments. We provide basic analysis and identify the critical factors to characterize the optimal controls and the corresponding plant–herbivore dynamics such as the control upper bound (the effectiveness level of the implementation of control measures) and the initial conditions of the plant and herbivore. Our results show that optimal control could be easier when the model has simple dynamics such as stable equilibrium dynamics under constant environment or the model exhibits chaotic dynamics under fluctuating environments. Due to bistability, initial conditions are important for optimal controls. Regardless of with or without fluctuating environments, initial conditions taken from the near the boundary makes optimal control easier. In general, the pest is hard to be eliminated when the control upper bound is not large enough. However, as the control upper bound is increased or the initial conditions are chosen from near the boundary of the basin of attractions, the pest can be manageable regardless of the fluctuating environments.

Autophagy is an essential lysosome-mediated degradation pathway that maintains cellular homeostasis and viability in response to various intra- and extracellular stresses. Mitophagy is a type of autophagy that is involved in the intricate removal of dysfunctional mitochondria during conditions of metabolic stress. In this review, we describe the multifaceted roles of autophagy and mitophagy in normal physiology and the field of cancer biology. Autophagy and mitophagy exhibit dual context-dependent roles in cancer development, acting as tumor suppressors and promoters. We also discuss the important role of autophagy and mitophagy within the cancer microenvironment and how autophagy and mitophagy influence tumor host–cell interactions to overcome metabolic deficiencies and sustain the activity of cancer-associated fibroblasts (CAFs) in a stromal environment. Finally, we explore the dynamic interplay between autophagy and the immune response in tumors, indicating their potential as immunomodulatory targets in cancer therapy. As the field of autophagy and mitophagy continues to evolve, this comprehensive review provides insights into their important roles in cancer and cancer microenvironment.

Machine Learning is a powerful tool to discover hidden information and relationships in various data-driven research fields. Obesity is an extremely complex topic, involving biological, physiological, psychological, and environmental factors. One successful approach to the topic is machine learning frameworks, which can reveal complex and essential risk factors of obesity. Over the last two decades, the obese population (BMI of above 23) in Korea has grown. The purpose of this study is to identify risk factors that predict obesity using machine learning classifiers and identify the algorithm with the best accuracy among classifiers used for obesity prediction. This work will allow people to assess obesity risk from blood tests and blood pressure data based on the KNHANES, which used data constructed by the annual survey. Our data include a total of 21,100 participants (male 10,000 and female 11,100). We assess obesity prediction by utilizing six machine learning algorithms. We explore age- and gender-specific risk factors of obesity for adults (19–79 years old). Our results highlight the four most significant features in all age-gender groups for predicting obesity: triglycerides, ALT (SGPT), glycated hemoglobin, and uric acid. Our findings show that the risk factors for obesity are sensitive to age and gender under different machine learning algorithms. Performance is highest for the 19–39 age group of both genders, with over 70% accuracy and AUC, while the 60–79 age group shows around 65% accuracy and AUC. For the 40–59 age groups, the proposed algorithm achieved over 70% in AUC, but for the female participants, it achieved lower than 70% accuracy. For all classifiers and age groups, there is no big difference in the accuracy ratio when the number of features is more than six; however, the accuracy ratio decreased in the female 19–39 age group.

Abstract A novel severe acute respiratory syndrome coronavirus 2 emerged in December 2019, and it took only a few months for WHO to declare COVID-19 as a pandemic in March 2020. It is very challenging to discover complex spatial–temporal transmission mechanisms. However, it is crucial to capture essential features of regional-temporal patterns of COVID-19 to implement prompt and effective prevention or mitigation interventions. In this work, we develop a novel framework of compatible window-wise dynamic mode decomposition (CwDMD) for nonlinear infectious disease dynamics. The compatible window is a selected representative subdomain of time series data, in which compatibility between spatial and temporal resolutions is established so that DMD can provide meaningful data analysis. A total of four compatible windows have been selected from COVID-19 time-series data from January 20, 2020, to May 10, 2021, in South Korea. The spatiotemporal patterns of these four windows are then analyzed. Several hot and cold spots were identified, their spatial–temporal relationships, and some hidden regional patterns were discovered. Our analysis reveals that the first wave was contained in the Daegu and Gyeongbuk areas, but it spread rapidly to the whole of South Korea after the second wave. Later on, the spatial distribution is seen to become more homogeneous after the third wave. Our analysis also identifies that some patterns are not related to regional relevance. These findings have then been analyzed and associated with the inter-regional and local characteristics of South Korea. Thus, the present study is expected to provide public health officials helpful insights for future regional-temporal specific mitigation plans.

OBJECTIVES This study aimed to investigate the factors affecting cancer survival and develop a mortality prediction model for Korean cancer survivors. Our study identified lifestyle and mortality risk factors and attempted to determine whether health-promoting lifestyles affect mortality. METHODS Among the 1,637,287 participants in the Korean Cancer Prevention Study (KCPS) cohort, 200,834 cancer survivors who were alive after cancer diagnosis were analyzed. Discrimination and calibration for predicting the 10-year mortality risk were evaluated. A prediction model was derived using the Cox model coefficients, mean risk factor values, and mean mortality from the cancer survivors in the KCPS cohort. RESULTS During the 21.6-year follow-up, the all-cause mortality rates of cancer survivors were 57.2% and 39.4% in men and women, respectively. Men, older age, current smoking, and a history of diabetes were high-risk factors for mortality, while exercise habits and a family history of cancer were associated with reduced risk. The prediction model discrimination in the validation dataset for both KCPS all-cause mortality and KCPS cancer mortality was shown by C-statistics of 0.69 and 0.68, respectively. Based on the constructed prediction models, when we modified exercise status and smoking status, as modifiable factors, the cancer survivors’ risk of mortality decreased linearly. CONCLUSIONS A mortality prediction model for cancer survivors was developed that may be helpful in supporting a healthy life. Lifestyle modifications in cancer survivors may affect their risk of mortality in the future.

Human exposure to carcinogenic volatile organic compounds (VOCs), such as benzene, from hand sanitizers is a topic of current concern. In light of the heavy use of hand sanitizers during the COVID-19 pandemic, determination of exposure to toxicants present in these products deserves attention. The US Food and Drug Administration (FDA) had set an interim limit for benzene in alcohol-based hand sanitizers at 2000 parts-per-billion (ppb). We determined the concentrations of and exposure to three VOCs namely, benzene, toluene and styrene, in 200 hand sanitizers using high-resolution gas chromatography coupled with high-resolution mass spectrometry (HRGC-HRMS). Benzene, toluene and styrene were found in 31%, 25% and 32%, respectively, of the samples analyzed at mean concentrations of 395 (range: 0.181–22,300), 164 (range: 0.074–20,700) and 61.3 ng/g (range: 0.082–4200 ng/g), respectively. Benzene was found at concentrations > 2000 ng/g (above the FDA interim limit) in 5% of the samples, representing 9 brands. The mean potential dermal exposure doses (DEDs) to benzene (children/teenagers: 34.6; adults: 24.7 ng/kg-bw/d) were higher than those for toluene (children/teenagers: 14.4; adults: 10.3 ng/kg-bw/d) and styrene (children/teenagers: 5.37; adults: 3.83 ng/kg-bw/d) in the 200 hand sanitizers analyzed. The estimated cancer risk from exposure to benzene in children/teenagers and adults from hand sanitizer use (at an estimated usage rate of 5 g/day) was greater than the one-in-a-million risk benchmark (1.0 × 10–6) for 10% and 9% of the samples, respectively. To the best of our knowledge, this is the first study to determine both the concentrations of and exposure risks to benzene, toluene and styrene present in hand sanitizers.

Although the primary and secondary vaccination rates in Korea account for over 75% of the total population, confirmed cases of COVID-19 are dramatically increasing due to immune waning and the Omicron variant. Therefore, it is urgent to evaluate the effectiveness of booster vaccination strategies for living with COVID-19. In this work, we have developed an age-specific mathematical model with eight age groups and included age-specific comorbidities to evaluate the effectiveness of age-specific vaccination prioritization strategies to minimize morbidity and mortality. Furthermore, we have investigated the impacts of age-specific vaccination strategies for different vaccine supplies and non-pharmaceutical intervention levels during two periods: (1) when vaccine supply was insufficient and (2) after the emergence of the omicron variant. During the first period, the best option was to vaccinate the 30–49 year age group and the group with comorbidities to minimize morbidity and mortality, respectively. However, a booster vaccination should prioritize the 30–49 year age group to promote both minimal morbidity and mortality. Critical factors, such as vaccination speed, vaccine efficacy, and non-pharmaceutical interventions (NPIs), should be considered for effective vaccination prioritization as well. Primary, secondary vaccinations, and a booster shot vaccinations require different age prioritization strategies under different vaccination rates, vaccine efficacies, and NPI levels.

Recent COVID-19 outbreaks pose serious public health challenges all around the world. South Korea had experienced the early outbreak of the COVID-19 pandemic and implemented early effective interventions. The 2020 COVID-19 outbreak in South Korea showed spatial hot spots and super-spreading events. As a result of these super-spreading events, three huge outbreaks of the COVID-19 have occurred in Korea from February to December 2020. To capture the intrinsic nature of heterogeneity, an agent-based model has been developed focusing on early transmission dynamics of COVID-19 in South Korea. Based on the social empirical contact information of early confirmed cases of COVID-19, we have constructed a scale-free network. Our agent-based model has incorporated essential individual variability such as different contact numbers and infectivity levels. In the absence of vaccines or treatment, contact tracing, case-isolation, quarantine are the most critical interventions to prevent larger outbreaks. First, we investigate the impacts of critical factors on various epidemic outputs such as incidence and cumulative incidence. These critical factors include contact numbers, transmission rates, infectivity of presymptomatic or asymptomatic cases, and contact-tracing with quarantine intervention. Furthermore, the effectiveness of case isolation and contact-tracing (followed by quarantine) is evaluated under various scenarios. Our results indicate that case isolation combined with contact-tracing quarantine is much more effective under a moderate level of $\mathcal R_{0}$ (smaller transmission rates or contact numbers) and presymptomatic cases. However, the efficacy of interventions reduces significantly for a higher level of $\mathcal R_{0}$ (larger transmission rates or contact numbers) with a high level of infectivity (in presymptomatic cases). This highlights the key role of efficient contact-tracing and case-isolation to mitigate larger outbreaks or super-spreading events.

The goal of the current study was to explore the visual representation of #ShoutYourAbortion hashtag movement on Instagram. The photos’ content and embedded texts in the photos were examined. And the photos were clustered using k -means clustering algorithm, and the resulting clustered were compared using the same criteria above. Photo features which shows the content- and pixel-level characteristics were extracted and used for comparison between clusters. The photo features were also used to examine their relationships with the public’s responses. It was found that text was the main type of content, and the texts presented in photos were mainly about stories told in first person point of view as a woman. The photos were grouped into two clusters, which differed in terms of content and photo features. And the public’s responses were found to be related to photo features. The results are expected to contribute to the understanding of hashtag movements via photos and making photos in hashtag movements more appealing to the public.

The series is one of the most popular formats of television programs, but the emotional aspects of its content have not attracted much attention from researchers. Furthermore, the cinemetric approach, in which move data are quantitively examined, has not been used to analyze affective content in television series. Therefore, this study focused on emotion distribution in Korean television series. In all, 337 episodes from 20 series were divided into shots, then keyframes were selected from the shots. Facial emotions on the keyframes were measured using an online artificial intelligence service. Emotion distributions were obtained from each episode and then were observed for common patterns. In addition, the statistical complexity of the distributions were calculated and compared by the temporal phase of the episode, channel type, genre, ratings, and season. The results show that the emotion distributions of all episodes in all series had the same form: an asymmetric U-shaped distribution biased toward zero. The statistical complexity of emotion distribution was greater in early episodes than in later episodes of a series, and it was higher in episodes with higher ratings. In addition, the complexity of emotion distribution was higher in hard genres and during the summer season. There was no significant difference between terrestrial television channels and cable/general programming channels.

The epidemiology and transmission dynamics of infectious diseases must be understood at the individual and community levels to improve public health decision-making for real-time and integrated community-based control strategies. Herein, we explore the epidemiological characteristics for assessing the impact of public health interventions in the community setting and their applications. Computational statistical methods could advance research on infectious disease epidemiology and accumulate scientific evidence of the potential impacts of pharmaceutical/nonpharmaceutical measures to mitigate or control infectious diseases in the community. Novel public health threats from emerging zoonotic infectious diseases are urgent issues. Given these direct and indirect mitigating impacts at various levels to different infectious diseases and their burdens, we must consider an integrated assessment approach, ‘One Health’, to understand the dynamics and control of infectious diseases.

Vascular disease: Blood flow disturbances and atherosclerosis Research into the mechanisms by which blood flow disturbances affect the function of endothelial cells (ECs), the cells lining the interior of blood vessels, reveals potential new targets for treating atherosclerosis. Kihwan Kwon at Ewha Womans University in Seoul, South Korea, and colleagues found that a membrane protein, the coxsackie and adenovirus receptor, CAR, mediates the response of ECs to the shear stress exerted by blood flow. They showed, in human tissue and in mice, that CAR protein levels in ECs increase when they are exposed to low or oscillatory blood flow, which is linked to the build-up of plaque inside arteries. Lowering CAR levels in ECs reduced the expression of proinflammatory genes and the formation of atherosclerotic lesions in mice. These findings suggest that reducing CAR activity could be a promising approach for treating atherosclerosis.

Background : The prevalence of metabolic syndrome is increasing in Korea, particularly among young adults. This trend will increase the incidence of cardiovascular and metabolic diseases in the future. Therefore, it is imperative to detect and prevent metabolic abnormalities early in life. Here, we established a hospital-based biobank cohort to identify the most prevalent dysmetabolic phenotype. The aim of this report was to inform other researchers of our protocol and to share our data for future collaboration. Methods : The baseline examination comprised health-related questionnaires, anthropometric and handgrip strength measurements, bioelectrical impedance analysis of body composition, and nutritional assessment. Relevant biochemical parameters were measured, and oral glucose tolerance tests were performed. Metabolic syndrome was defined according to the National Cholesterol Education Program Adult Treatment Panel III criteria with Asian waist circumference criteria. Results : From a total of about 3,000 employees aged 20 to 59 years working at Seoul National University Bundang Hospital, 1,017 were enrolled from 2015 to 2016. The mean age was 34.5±8.4 years for men (n=311, 30.6%) and 30.9±8.5 years for women (n=706, 69.4%). The overall prevalence of metabolic syndrome was 7.6% (17.7% in men and 3.1% in women). Among the five components of metabolic syndrome, high blood pressure was the most prevalent in both men (51.4%) and women (13.8%). Conclusion : Although further follow-up data are needed, we expect that more adverse cardiovascular events may occur in men than in women. This hospital-based cohort will serve as the foundation for a comprehensive evaluation of metabolic syndrome and future cardiometabolic disease risk in middle-aged Koreans.

Background: Persistent organic pollutants (POPs) are linked with insulin resistance and type-2 diabetes (T2D) in the general population. However, their associations with gestational diabetes (GDM) are inconsistent. Objective: We prospectively evaluated the associations of POPs measured in early pregnancy with GDM risk. We also assessed whether pre-pregnancy BMI (ppBMI) and family history of T2D modify this risk. Methods: In NICHD Fetal Growth Study, Singletons, we measured plasma concentration of 76 POPs, including 11 organochlorine pesticides (OCPs), 9 polybrominated diphenylethers (PBDEs), 44 polychlorinated biphenyls (PCBs), and 11 per-and polyfluoroalkyl substances (PFAS) among 2334 healthy non-obese women at 8–13 weeks of gestation. GDM was diagnosed by Carpenter and Coustan criteria. We constructed chemical networks using a weighted-correlation algorithm and examined the associations of individual chemical and chemical networks with GDM using multivariate Poisson regression with robust variance. Results: Higher concentrations of PCBs with six or more chlorine atoms were associated with increased risk of GDM in the overall cohort (risk ratios [RRs] range: 1.08–1.13 per 1-standard deviation [SD] increment) and among women with a family history of T2D (RRs range: 1.08–1.48 per 1-SD increment) or normal ppBMI (RRs range: 1.08–1.22 per 1-SD increment). Similar associations were observed for the chemical network comprised of PCBs with ≥6 chlorine atoms and the summary measure of total PCBs and non-dioxin like PCBs (138, 153, 170, 180). Furthermore, four PFAS congeners - perfluorononanoic acid (PFNA), perfluorooctanoic acid (PFOA), perfluoroheptanoic acid (PFHpA), and perfluorododecanoic acid (PFDoDA) - showed significant positive associations with GDM among women with a family history of T2D (RRs range:1.22–3.18 per 1-SD increment), whereas BDE47 and BDE153 showed significant positive associations among women without a family history of T2D. Conclusions: Environmentally relevant levels of heavily chlorinated PCBs and some PFAS and PBDEs were positively associated with GDM with suggestive effect modifications by family history of T2D and body adiposity status. Keywords: Persistent Organic Pollutants (POPs), Gestational Diabetes Mellitus (GDM), Organochlorine pesticide (OCP), Polybrominated diphenylether (PBDE), Polychlorinated biphenyl (PCB), Per-and polyfluoroalkyl substances (PFAS)

Zoonotic avian influenza viruses pose severe health threats to humans. Of several viral subtypes reported, the low pathogenic avian influenza H7N9 virus has since February 2013 caused more than 1,500 cases of human infection with an almost 40% case-fatality rate. Vaccination of poultry appears to reduce human infections. However, the emergence of highly pathogenic strains has increased concerns about H7N9 pandemics. To develop an efficacious H7N9 human vaccine, we designed vaccine viruses by changing the patterns of N-linked glycosylation (NLG) on the viral hemagglutinin (HA) protein based on evolutionary patterns of H7 HA NLG changes. Notably, a virus in which 2 NLG modifications were added to HA showed higher growth rates in cell culture and elicited more cross-reactive antibodies than did other vaccine viruses with no change in the viral antigenicity. Developed into an inactivated vaccine formulation, the vaccine virus with 2 HA NLG additions exhibited much better protective efficacy against lethal viral challenge in mice than did a vaccine candidate with wild-type (WT) HA by reducing viral replication in the lungs. In a ferret model, the 2 NLG-added vaccine viruses also induced hemagglutination-inhibiting antibodies and significantly suppressed viral replication in the upper and lower respiratory tracts compared with the WT HA vaccines. In a mode of action study, the HA NLG modification appeared to increase HA protein contents incorporated into viral particles, which would be successfully translated to improve vaccine efficacy. These results suggest the strong potential of HA NLG modifications in designing avian influenza vaccines.

The omicron variant (B.1.1.529) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was the predominant variant in South Korea from late January 2022. In this study, we aimed to report the early estimates of the serial interval distribution and reproduction number to quantify the transmissibility of the omicron variant in South Korea between 25 November 2021 and 31 December 2021. We analyzed 427 local omicron cases and reconstructed 73 transmission pairs. We used a maximum likelihood estimation to assess serial interval distribution from transmission pair data and reproduction numbers from 74 local cases in the first local outbreak. We estimated that the mean serial interval was 3.78 (standard deviation, 0.76) days, which was significantly shorter in child infectors (3.0 days) compared to adult infectors (5.0 days) (p < 0.01). We estimated the mean reproduction number was 1.72 (95% CrI, 1.60–1.85) for the omicron variant during the first local outbreak. Strict adherence to public health measures, particularly in children, should be in place to reduce the transmission risk of the highly transmissible omicron variant in the community.

Recurrent outbreaks of the influenza virus continue to pose a serious health threat all over the world. The role of mass media becomes increasingly important in modeling infectious disease transmission dynamics since it can provide public health information that influences risk perception and health behaviors. Motivated by the recent 2009 H1N1 influenza pandemic outbreak in South Korea, a mathematical model has been developed. In this work, a previous influenza transmission model is modified by incorporating two distinct media effect terms in the transmission rate function; (1) a theory-based media effect term is defined as a function of the number of infected people and its rage of change and (2) a data-based media effect term employs the real-world media coverage data during the same period of the 2009 influenza outbreak. The transmission rate and the media parameters are estimated through the least-squares fitting of the influenza model with two media effect terms to the 2009 H1N1 cumulative number of confirmed cases. The impacts of media effect terms are investigated in terms of incidence and cumulative incidence. Our results highlight that the theory-based and data-based media effect terms have almost the same influence on the influenza dynamics under the parameters obtained in this study. Numerical simulations suggest that the media can have a positive influence on influenza dynamics; more media coverage leads to a reduced peak size and final epidemic size of influenza.

In this study, numerical simulations using the Environmental Fluid Dynamics Code model were conducted to elucidate the effects of flow structures in the recirculation zone on solute storage based on the junction angle. Numerical simulations were performed at a junction angle of 30° to 90° with a momentum flux ratio of 1.62. The simulation results revealed that an increase in the junction angle caused the recirculation zone length and width to increase and strengthened the development of helical motion. The helical motion increased the vertical gradient of the mixing layer and the mixing metric of the dosage curves. The recirculation zone accumulated the solute as a storage zone, which formed a long tail in the concentration curves. The interaction between the helical motion and recirculation zone affected the transverse mixing, such that the transverse dispersion had a positive relationship with the helical motion intensity and a negative relationship with the recirculation zone size. Transverse mixing exhibited an inverse relationship with the mass exchange rate of the recirculation zone. These results indicate that the transverse dispersion is replaced by mixing due to strongly developed storage zones.

2-Cys peroxiredoxin (Prx) enzymes are highly expressed in most cancers but how they promote cancer progression is unclear. Here the authors show that in colorectal cancers with APC mutation, PrxII binds to tankyrase and prevents its oxidative inactivation, thereby preventing Axin1-dependent degradation of ²b-catenin.

ObjectiveTo examine the effect of smoking on risk of atherosclerotic cardiovascular disease (ASCVD) in Korean young men and to examine whether serum total cholesterol levels could modify the effect of smoking on ASCVD.DesignA prospective cohort study within a national insurance system.SettingHealth screenings provided by national insurance in 1992 and 1994.ParticipantsA total of 118 531 young men between 20 and 29 years of age and were followed up for an average of 23 years.Outcome measureTo assess the independent effects of smoking on the risk of ischaemic heart disease (IHD), stroke and ASCVD, Cox proportional hazards regression models were used, controlling for age, hypertension, diabetes, hypercholesterolaemia and alcohol drinking.ResultsThe total number of current smokers was 78 455 (66.2%), and 94 113 (79.7%) of the sample recorded a total cholesterol level

Summary: There are abundant peroxiredoxin (Prx) enzymes, but an increase of cellular H2O2 level always happens in apoptotic cells. Here, we show that cellular H2O2 switches different apoptosis pathways depending on which type of Prx enzyme is absent. TNF-α-induced H2O2 burst preferentially activates the DNA damage-dependent apoptosis pathway in the absence of PrxI. By contrast, the same H2O2 burst stimulates the RIPK1-dependent apoptosis pathway in the absence of PrxII by inducing the destruction of cIAP1 in caveolar membrane. Specifically, H2O2 induces the oxidation of Cys308 residue in the cIAP1-BIR3 domain, which induces the dimerization-dependent E3 ligase activation. Thus, the reduction in cIAP level by the absence of PrxII triggers cell-autonomous apoptosis in cancer cells and tumors. Such differential functions of PrxI and PrxII are mediated by interaction with H2AX and cIAP1, respectively. Collectively, this study reveals the distinct switch roles of 2-Cys Prx isoforms in apoptosis signaling. : Lee et al. show that the 2-Cys peroxiredoxin (Prx) isoforms, Prx I and Prx II, discretely regulate different apoptosis pathways. Specifically, the absence of Prx I augments apoptosis through the DNA damage response, while the absence of Prx II switches RIPK1-dependent apoptosis by causing cIAP1 depletion. Keywords: peroxiredoxin, apoptosis, H2O2, TNF-α, cIAP, DNA damage, RIPK1

Korea has the highest burden of tuberculosis (TB) among Organization for Economic Co-operation and Development countries. Various strategies have been implemented to eradicate TB in Korea, and it is critical to evaluate previous TB management outcomes before framing future TB policies. Over the past few decades, the rapid increase in the aging population in Korea has substantially impacted the incidence of TB among the elderly. Thus, in this study, we aimed to develop a mathematical model for the assessment of TB management outcomes incorporating special features of TB transmission dynamics in Korea. First, we incorporate 2-age groups in our TB model because TB epidemics in Korea are different between the elderly and the non-elderly (

The transdermal delivery system of nutrients, cosmetics, and drugs is particularly attractive for painless, noninvasive delivery and sustainable release. Recently, atmospheric pressure plasma techniques have been of great interest to improve the drug absorption rate in transdermal delivery. Currently, plasma-mediated changes in the lipid composition of the stratum corneum are considered a possible mechanism to increase transdermal permeability. Nevertheless, its molecular and cellular mechanisms in transdermal delivery have been largely confined and still veiled. Herein, we present the effects of cold plasma on transdermal transmission on porcine skin and the cellular permeability of keratinocytes and further demonstrate the production of nitric oxide from keratinocytes. Consequently, argon plasma irradiation for 60 s resulted in 2.5-fold higher transdermal absorption of aniline blue dye on porcine skin compared to the nontreated control. In addition, the plasma-treated keratinocytes showed an increased transmission of high-molecular-weight molecules (70 and 150 kDa) with the production of nitric oxide. Therefore, these findings suggest a promoting effect of low-temperature plasma on transdermal absorption, even for high-molecular-weight molecules. Moreover, plasma-induced nitric oxide from keratinocytes is likely to regulate transdermal permeability in the epidermal layer.

Abstract Background No previous study has explored the interactions between education and lifestyle in relation to obesity. This study hypothesized that education may be obesogenic through its interplay with lifestyle behaviors. Methods Data for a nationally representative sample (6937 men and 9333 women) from the Korea National Health and Nutrition Examination Survey (2010–2012) were analyzed. Multivariate logistic regressions were performed for three education levels and six lifestyle behaviors, each of which comprised two groups. Results Interactions between education and lifestyle behaviors in relation to obesity were observed for all lifestyle behaviors in women (p for interaction

We consider the generalized fifth-order KdV type nonlinear evolution equation with variable coefficients. The system technique has been applied rigorously in order to find new exact solutions of the considered equations. The closed-form solutions of the fifth-order KdV type nonlinear evolution equation are expressed by the proposed ansa¨tz in terms of exponential functions. We believe that the system technique is effective and stable of finding the exact solutions of nonlinear evolution equations. Further, we describe the behaviors of the obtained solutions under certain constraints and variable coefficients. Keywords: Nonlinear KdV-type equation, Variable coefficient, Explicit solution, System technique

Nowadays, the sizes of pixel sensors in digital cameras are decreasing as the resolution of the image sensor increases. Due to the decreased size, the pixel sensors receive less light energy, which makes it more sensitive to thermal noise. Even a small amount of noise in the color filter array (CFA) can have a significant effect on the reconstruction of the color image, as two-thirds of the missing data would have to be reconstructed from noisy data; because of this, direct denoising would need to be performed on the raw CFA to obtain a high-resolution color image. In this paper, we propose an interchannel nonlocal weighted moving least square method for the noise removal of the raw CFA. The proposed method is our first attempt of applying a two dimensional (2-D) polynomial approximation to denoising the CFA. Previous works make use of 2-D linear or directional 1-D polynomial approximations. The reason that 2-D polynomial approximation methods have not been applied to this problem is the difficulty of the weight control in the 2-D polynomial approximation method, as a small amount of noise can have a large effect on the approximated 2-D shape. This makes CFA denoising more important, as the approximated 2-D shape has to be reconstructed from only one-third of the original data. To address this problem, we propose a method that reconstructs the approximated 2-D shapes corresponding to the RGB color channels based on the measure of the similarities of the patches directly on the CFA. By doing so, the interchannel information is incorporated into the denoising scheme, which results in a well-controlled and higher order of polynomial approximation of the color channels. Compared to other nonlocal-mean-based denoising methods, the proposed method uses an extra reproducing constraint, which guarantees a certain degree of the approximation order; therefore, the proposed method can reduce the number of false reconstruction artifacts that often occur in nonlocal-mean-based denoising methods. Experimental results demonstrate the performance of the proposed algorithm.

Dengue fever is a major international public health concern, with more than 55% of the world population at risk of infection. Recent climate changes related to global warming have increased the potential risk of domestic outbreaks of dengue in Korea. In this study, we develop a two-strain dengue model associated with climate-dependent parameters based on Representative Concentration Pathway (RCP) scenarios provided by the Korea Meteorological Administration. We assess the potential risks of dengue outbreaks by means of the vector capacity and intensity under various RCP scenarios. A sensitivity analysis of the temperature-dependent parameters is performed to explore the effects of climate change on dengue transmission dynamics. Our results demonstrate that a higher temperature significantly enhances the potential threat of domestic dengue outbreaks in Korea. Furthermore, we investigate the effects of countermeasures on the cumulative incidence of humans and vectors. The current main control measures (comprising only travel restrictions) for infected humans in Korea are not as effective as combined control measures (travel restrictions and vector control), dramatically reducing the possibilities of dengue outbreaks.

No study of obesity risk for people in developed countries has conducted a multi-dimensional analysis of the association of socioeconomic status with obesity. In this paper, we investigated if education functions as either a confounder or an effect modifier in the association of another socioeconomic status indicator with obesity.This cross-sectional study analyzed data of an adult population sample (10,905 men and 14,580 women) from the Korea National Health and Nutrition Examination Survey (2010-2014). The study performed multivariate logistic regression analyses for three education levels and four indicators of socioeconomic status (i.e., marital status, residential area, occupation, and income).The overall prevalence of obesity was 38.1% in men and 29.1% in women (p < 0.001). In men, while education functioned as an effect modifier in the association between marital status and obesity (p for interaction = 0.006), it functioned as both a confounder (p < 0.001) and an effect modifier (p for interaction < 0.001) in the association between residential area and obesity. In contrast, in women, education functioned as a confounder in the association of residential area with obesity (p = 0.010). However, it functioned as both a confounder (p < 0.001) and an effect modifier (p for interaction = 0.012) in the association between income and obesity. A prediction showed that unlike in women, education was positively associated with obesity risk for some socioeconomic indicator groups in men; for example, in a rural resident group, a higher level of education increased the probability of being obese by 19.7%.The present study suggests the need to examine sex-specific studies regarding the role of education on the association between other socioeconomic status indicators and obesity. This should be considered in planning education policies to reduce the risk of obesity.

Emerging and re-emerging dengue fever has posed serious problems to public health officials in many tropical and subtropical countries. Continuous traveling in seasonally varying areas makes it more difficult to control the spread of dengue fever. In this work, we consider a two-patch dengue model that can capture the movement of host individuals between and within patches using a residence-time matrix. A previous two-patch dengue model without seasonality is extended by adding host demographics and seasonal forcing in the transmission rates. We investigate the effects of human movement and seasonality on the two-patch dengue transmission dynamics. Motivated by the recent Peruvian dengue data in jungle/rural areas and coast/urban areas, our model mimics the seasonal patterns of dengue outbreaks in two patches. The roles of seasonality and residence-time configurations are highlighted in terms of the seasonal reproduction number and cumulative incidence. Moreover, optimal control theory is employed to identify and evaluate patch-specific control measures aimed at reducing dengue prevalence in the presence of seasonality. Our findings demonstrate that optimal patch-specific control strategies are sensitive to seasonality and residence-time scenarios. Targeting only the jungle (or endemic) is as effective as controlling both patches under weak coupling or symmetric mobility. However, focusing on intervention for the city (or high density areas) turns out to be optimal when two patches are strongly coupled with asymmetric mobility.

BackgroundThe purpose of this study is to analyze the scale of and trends associated with the cost-of-illness of thyroid disease in Korea at 2-year intervals during the last 10 years for which data are available.MethodsCost-of-illness was estimated in terms of direct and indirect costs. Direct costs include direct medical costs due to hospitalization, outpatient and pharmacy sectors, transportation, and care-giver costs. Indirect costs include future income loss due to premature death and loss of productivity as a result of absence from work.ResultsThe cost-of-illness of thyroid disease in Korea was estimated at 224.2 billion won in 2002, 303.4 billion won in 2004, 400.3 billion won in 2006, 570.4 billion won in 2008, and 762.2 billion won in 2010. For example, the cost-of-illness of thyroid disease in 2010 was 3.4 times greater compared to 2002. The direct cost of the total cost-of-illness was 69.7%, which accounted for the highest proportion of costs. Cost-of-illness for individuals between the ages of 30 and 50 accounted for the greatest share of costs.ConclusionThe cost-of-illness of thyroid disease was relatively large in economically active age groups, and demonstrated a very rapid growth rate compared to other major diseases in Korea. Therefore, we suggest nationwide recognition of the importance of prevention and management of thyroid disease and prioritization of the management of thyroid disease among current and future health promotion policies in Korea.

Cancer cells are abnormal cells that do not comply with tissue homeostasis but undergo uncontrolled proliferation. Such abnormality is driven mostly by somatic mutations on oncogenes and tumor suppressors. Cancerous mutations show intra-tumoral heterogeneity across cancer types and eventually converge into the self-activation of proliferative signaling. While transient production of intracellular reactive oxygen species (ROS) is essential for cell signaling, its persistent production is cytotoxic. Thus, cancer cells require increased levels of intracellular ROS for continuous proliferation, but overexpress cellular peroxidase enzymes, such as 2-Cys peroxiredoxins, to maintain ROS homeostasis. However, suppression of 2-Cys peroxiredoxins has also been reported in some metastatic cancers. Hence, the cancer-associated functions of 2-Cys peroxiredoxins must be illuminated in the cellular context. In this review, we describe the distinctive signaling roles of 2-Cys peroxiredoxins beyond their intrinsic ROS-scavenging role in relation to cancer cell death and survival.

The forced Korteweg-de Vries equation is considered to investigate the impact of bottom configurations on the free surface waves in a two-dimensional channel flow. In the study of shallow water waves, the bottom topography plays a critical role, which can determine the characteristics of wave motions significantly. The interplay between solitary waves and the bottom topography can exhibit more interesting dynamics of the free surface waves when the bottom configuration is more complex. In the presence of two bumps, there are multiple trapped solitary wave solutions, which remain stable between two bumps up to a finite time when they evolve in time. In this work, various stationary trapped wave solutions of the forced KdV equation are explored as the bump sizes and the distance between two bumps are varied. Moreover, the semi-implicit finite difference method is employed to study their time evolutions in the presence of two-bump configurations. Our numerical results show that the interplay between trapped solitary waves and two bumps is the key determinant which influences the time evolution of those wave solutions. The trapped solitary waves tend to remain between two bumps for a longer time period as the distance between two bumps increases. Interestingly, there exists a nontrivial relationship between the bump size and the time until trapped solitary waves remain stable between two bumps.

Mammalian cells have a well-defined set of antioxidant enzymes, which includes superoxide dismutases, catalase, glutathione peroxidases, and peroxiredoxins. Peroxiredoxins are the most recently identified family of antioxidant enzymes that catalyze the reduction reaction of peroxides, such as H2O2. In particular, typical 2-Cys peroxiredoxins are the featured peroxidase enzymes that receive the electrons from NADPH by coupling with thioredoxin and thioredoxin reductase. These enzymes distribute throughout the cellular compartments and, therefore, are thought to be broad-range antioxidant defenders. However, recent evidence demonstrates that typical 2-Cys peroxiredoxins play key signal regulatory roles in the various signaling networks by interacting with or residing near a specific redox-sensitive molecule. These discoveries help reveal the redox signaling landscape in mammalian cells and may further provide a new paradigm of therapeutic approaches based on redox signaling.