• Kidney Research and Clinical Practice
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• v.36 no.1
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• pp.48-57
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• 2017

Background: Hepatic steatosis measured with controlled attenuation parameter (CAP) using transient elastography predicts metabolic syndrome in the general population. We investigated whether CAP predicted metabolic syndrome in chronic kidney disease patients. Methods: CAP was measured with transient elastography in 465 predialysis chronic kidney disease patients (mean age, 57.5 years). Results: The median CAP value was 239 (202-274) dB/m. In 195 (41.9%) patients with metabolic syndrome, diabetes mellitus was more prevalent (105 [53.8%] vs. 71 [26.3%], P < 0.001), with significantly increased urine albumin-to-creatinine ratio (184 [38-706] vs. 56 [16-408] mg/g Cr, P = 0.003), high sensitivity C-reactive protein levels (5.4 [1.4-28.2] vs. 1.7 [0.6-9.9] mg/L, P < 0.001), and CAP (248 [210-302] vs. 226 [196-259] dB/m, P < 0.001). In multiple linear regression analysis, CAP was independently related to body mass index (${\beta}=0.742$, P < 0.001), triglyceride levels (${\beta}=2.034$, P < 0.001), estimated glomerular filtration rate (${\beta}=0.316$, P = 0.001), serum albumin (${\beta}=1.386$, P < 0.001), alanine aminotransferase (${\beta}=0.064$, P = 0.029), and total bilirubin (${\beta}=-0.881$, P = 0.009). In multiple logistic regression analysis, increased CAP was independently associated with increased metabolic syndrome risk (per 10 dB/m increase; odds ratio, 1.093; 95% confidence interval, 1.009-1.183; P = 0.029) even after adjusting for multiple confounding factors. Conclusion: Increased CAP measured with transient elastography significantly correlated with and could predict increased metabolic syndrome risk in chronic kidney disease patients.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.107-116
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• 2021

Purpose : To examine the relationship among osteoporosis knowledge, osteoporosis preventive behaviors, self-efficacy and health-related quality of life of middle aged women and to find out factors which influence health-related quality of life. Methods : For study subjects, female patients aged 40 to 64 and hospitalized at the surgical wards of two general hospitals in G city were conveniently sampled. The data have been collected from January 11 to March 10, 2018. Data were analyzed by descriptive statistics, t-test, ANOVA, Scheffe's test, Pearson's correlation coefficient, and multiple regression. Results : The average score of osteoporosis knowledge was 12.50±3.47, the average score of osteoporosis preventive behaviors was 44.96±8.16 and the average score of osteoporosis self-efficacy was 40.38±8.07. The factors influencing EQ-5D Index in health-related quality of life were comorbidity, osteoporosis preventive behaviors, osteoporosis knowledge and average monthly income, which could account for health-related quality of life at 18.0%. The factors influencing EQ-5D VAS were osteoporosis preventive behaviors, self-efficacy, osteoporosis knowledge and age of menarche, which could account for health-related : Higher osteoporosis knowledge, osteoporosis preventive behaviors and self-efficacy, the better the subjects health-related quality of life. Therefore, as a way to promote health-related quality of life of middle aged women, the constant development and the application of a program which may promote osteoporosis knowledge, osteoporosis preventive behaviors and self-efficacy are needed.

• Korean Journal of Psychosomatic Medicine
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• v.29 no.2
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• pp.162-168
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• 2021

Objectives : Although dysfunctional breathing is a common symptom in general population and affects qualities of life, it is still underdiagnosed. There are some studies of prevalence of it in astma, but few studies in anxiety and depressive disorders. The purposes of this study were to explore the prevalence of it in anxiety and depressive disorders, and to investigate whether anxiety and depressed mood influence it. Methods : 135 patients diagnosed with anxiety or depressive disorders, and 124 controls were recruited. Nijmegen questionnaire was used to assess dysfunctional breathing, and Hospital anxiety depression scale was used. Results : The prevalence of dysfunctional breathing in anxiety or depressive disorders was higher than that in control. In the linear regression model, anxiety accounted for 59.6% of dysfunctional breathing, but depressed mood did not. With covariate adjusted for anxiety, scores of dysfunctional breathing in anxiety or depressive disorders were higher than in controls. Conclusions : Dysfunctional breathing in anxiety or depressive disorders is higher than that in control. Adjusting anxiety, its difference is still. Anxiety affects dysfunctional breathing, but depressed mood does not.

• Journal of Korean society of Dental Hygiene
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• v.18 no.6
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• pp.921-932
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• 2018

Objectives: The aim of this study was to investigate the number of existing permanent teeth and the denture usage status in elderly adults aged 65 years and above living in metropolitan cities and to confirm the degree of oral health inequality caused by the differences in oral conditions in each metropolitan city using the Lorenz curve and the Gini coefficient. Methods: The raw data for the analysis were obtained from the dataset of the sixth Korea National Health and Nutrition Examination Survey conducted between 2013 and 2015. The subjects included 1,764 people who underwent oral examination and answered questions. The complex samples general linear model was used to analyze the number of existing permanent teeth adjusted for age and monthly household income. The proportion of edentulousness and the denture status was analyzed using complex samples crosstabs. Results: The number of existing permanent teeth in the elderly adults aged 65 years and above was lowest in Ulsan (15.41) and highest in Gwangju (20.44). The proportion of edentulousness was highest in Busan (14.5%) and lowest in Daejeon (4.0%). With regard to the proportion of denture users, Busan had the highest tendency for denture usage (50.4%) and Gwangju had the lowest tendency (34.9) (p=0.172). The Gini's coefficient for the number of existing teeth was lowest in Busan (0.332). Oral health inequality was most severe in metropolitan cities. Conclusions: We found that oral health inequality exists among elderly adults living in the metropolitan cities of Korea using the Lorenz curve and Gini's coefficient.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.4
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• pp.585-591
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• 2019

Objective: This study investigated carcass characteristics and meat quality of purebred Pakchong 5, crossbred pigs sired by Pakchong 5, and crossbred pigs sired by Duroc. Methods: Forty-eight pigs (average body weight of 22.25 kg) were composed of three groups as purebred Pakchong 5 (PP), Large $White{\times}Landrace$ pigs sired by Pakchong 5 (LWLRP), and Large $White{\times}Landrace$ pigs sired by Duroc (LWLRD). Each group consisted of eight gilts and eight barrows. At 109-day-raising period, pigs were slaughtered, and carcass characteristics were evaluated. Longissimus thoracis (LT) muscles from left side of carcasses were evaluated for meat quality and chemical composition. Data were analyzed using general linear model procedure, where group, sex, and their interaction were included in the model. Results: The PP had greater carcass, total lean, and ham percentages than crossbred pigs (p<0.05). LWLRP had thicker backfat and more carcass fat percentage than LWLRD (p<0.05). There were no differences (p>0.05) on cutting percentages from tender loin, loin, boston butt, and picnic shoulder among groups. The PP and LWLRP had larger loin eye area (LEA) than LWLRD (p<0.05). Gilts had more loin percentage and lower $L^*$ value than barrows (p<0.05). No meat color parameters ($L^*$, $a^*$, and $b^*$) were affected by groups (p>0.05). PP and LWLRP had larger muscle fiber diameters than LWLRD (p<0.05). However, water holding capacity, Warner-Bratzler shear force values, and chemical composition of LT were not affected by group or sex (p>0.05). Conclusion: Pakchong 5 purebred has good carcass and lean percentages. Compared to Duroc crossbred pigs, Pakchong 5 crossbreds have similar carcass and lean percentages, larger LEA, and slightly more carcass fat, with comparable meat quality and chemical composition. Pakchong 5 boars are more affordable for very small- to medium-scale pig producers.

• Journal of Environmental Health Sciences
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• v.48 no.6
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• pp.315-323
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• 2022

Background: Socioeconomical disadvantaged communities are more vulnerable to environmental chemical exposure and associated health effects. However, there is limited information on chemical exposure among vulnerable populations in Korea. Objectives: This study investigated chemical exposure among underprivileged populations. We measured urinary metabolites of phthalates in urban disadvantaged communities and investigated their correlations with residential environment factors and relative socioeconomic vulnerability. Methods: Urine samples were collected from 64 residents in a disadvantaged community in Seoul. A total of eight phthalate metabolites were analyzed by liquid chromatography-mass spectroscopy. Analytical method used by the Korean National Environmental Health Survey (KoNEHS) was employed. Covariate variance analysis and general linear regression adjusted with age, sex and smoking were performed. Results: Several phthalate metabolites, namely monomethyl phthalate (MMP), monoethyl phthalate (MEP), mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), and mono-n-butyl phthalate (MnBP) had higher levels than those reported in the adults of 4th KoNEHS. Notably, the MnBP level was higher in the lower socioeconomic group (geometric mean [GM]=47.3 ㎍/g creatinine) compared to non-recipients (GM=31.9 ㎍/g creatinine) and the national reference level (GM=22.0, 28.2 and 32.2 ㎍/g creatinine for adults, 60's and 70's, respectively.). When age, sex and smoking were adjusted, MEP and MnBP were significantly increased the lower socioeconomic group than non-recipients (p=0.014, p=0.023). The lower socioeconomic group's age of flooring were higher than non-recipients, not statistically significant. Conclusions: These results suggest that a relatively low income and aged flooring could be considered as risk factors for increased levels of phthalate metabolites in socioeconomic vulnerable populations.

• Journal of Environmental Health Sciences
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• v.49 no.1
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• pp.37-47
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• 2023

Background: Paraben is a widely used substance with a preservative effect found in various materials such as food, medicine, personal care products, and cosmetics. Objectives: This study was conducted to identify the level of urinary paraben concentrations (i.e., methyl-, ethyl-, and propyl-) among Korean adults and to explore the factors related with the exposure levels. Methods: We analyzed the third period (2015~2017) of the Korean National Environmental Health Survey (KoNEHS). R statistical software (version 4.1.1) was used to estimate representative values for the whole population with weight variables to reflect sampling design. Whether urinary concentrations tended to increase as the level of paraben exposure-related characteristics increased was tested and P_trend was calculated using general linear models. Results: Urinary concentrations of all three parabens (i.e., methyl-, ethyl- and propyl-) were higher in women than in men (P_trend<0.0001, 0.008, and <0.0001), and the values of methylparaben and propylparaben tended to increase as the age of subjects increased (P_trend<0.0001, and <0.0001). Urinary concentrations of methylparaben and propylparaben were associated with intensity of exercise (P_trend<0.001, and 0.004), and that of propylparaben was higher in non-smokers (P_trend=0.01). In terms of paraben exposure-related variables, urinary concentrations of parabens (i.e., methyl-, ethyl- and propyl-) increased as the daily average frequency of teeth-brushing (P_trend<0.0001, 0.03 and 0.0001), the frequency of use of hair products (P_trend=0.005, 0.05 and 0.04), the frequency of use of makeup products (P_trend<0.001, 0.001 and <0.001), and the frequency of use of antibacterial products (P_trend=0.005, 0.02 and 0.02) increased. Conclusions: In our study, urinary concentrations of all three parabens are associated with gender, teethbrushing, hair products, make-up products, and antibacterial products. Methyl- and proyl-parabens were associated with age and intensity of exercise, and propyl-paraben was associated with smoking.

• Animal Bioscience
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• v.35 no.10
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• pp.1489-1498
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• 2022

Objective: The objective of this study was to identify polymorphism in olfactomedin like 3 (OLFML3) gene, and association analysis with meat quality, carcass characteristics, retail meat cut, and fatty acid composition in sheep, and expression quantification of OLFML3 gene in phenotypically divergent sheep. Methods: A total of 328 rams at the age of 10 to 12 months with an average body weight of 26.13 kg were used. A novel polymorphism was identified using high-throughput sequencing in sheep and genotyping of OLFML3 polymorphism was performed using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Among 328 rams, 100 rams representing various sheep genotypes were used for association study and proc general linear model was used to analyse association between genotypes and phenotypic traits. Quantitative real-time polymerase chain reaction (qRT-PCR) was used for the expression analysis of OLFML3 mRNA in phenotypically divergent sheep population. Results: The findings revealed a novel polymorphism in the OLFML3 gene (g.90317673 C>T). The OLFML3 gene revealed three genotypes: CC, CT, and TT. The single nucleotide polymorphism (SNP) was found to be significantly (p<0.05) associated with meat quality traits such as tenderness and cooking loss; carcass characteristics such as carcass length; retail meat cut such as pelvic fat in leg, intramuscular fat in loin and tenderloin, muscle in flank and shank; fatty acids composition such as tridecanoic acid (C13:0), palmitoleic acid (C16:1), heptadecanoic acid (C17:0), ginkgolic acid (C17:1), linolenic acid (C18:3n3), arachidic acid (C20:0), eicosenoic acid (C20:1), arachidonic acid (C20:4n6), heneicosylic acid (C21:0), and nervonic acid (C24:1). The TT genotype was associated with higher level of meat quality, carcass characteristics, retail meat cut, and some fatty acids composition. However, the mRNA expression analysis was not different among genotypes. Conclusion: The OLFML3 gene could be a potential putative candidate for selecting higher quality sheep meat, carcass characteristics, retail meat cuts, and fatty acid composition in sheep.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.121-130
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• 2024

As internet and communication technology (ICT) is improved exponentially, types and amount of available data also increase. Even though data analysis including statistics is significant to utilize this large amount of data, there are inevitable limits to process various and complex data in general way. Meanwhile, there are many attempts to apply machine learning (ML) in various fields to solve the problems according to the enhancement in computational performance and increase in demands for autonomous systems. Especially, data processing for the model input and designing the model to solve the objective function are critical to achieve the model performance. Data processing methods according to the type and property have been presented through many studies and the performance of ML highly varies depending on the methods. Nevertheless, there are difficulties in deciding which data processing method for data analysis since the types and characteristics of data have become more diverse. Specifically, multi-variate data processing is essential for solving non-linear problem based on ML. In this paper, we present a multi-variate tabular data processing scheme for ML-aided data analysis by using Titanic dataset from Kaggle including various kinds of data. We present the methods like input variable filtering applying statistical analysis and normalization according to the data property. In addition, we analyze the data structure using visualization. Lastly, we design an ML model and train the model by applying the proposed multi-variate data process. After that, we analyze the passenger's survival prediction performance of the trained model. We expect that the proposed multi-variate data processing and visualization can be extended to various environments for ML based analysis.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.