• Asian Pacific Journal of Cancer Prevention
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• v.15 no.11
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• pp.4417-4422
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• 2014

ERCC2 is an essential component of the nucleotide excision repair pathway which is involved in the effective maintenance of genome integrity. Association studies on ERCC2 polymorphisms and glioma risk have yielded inconclusive results. This meta-analysis was performed to gain a better insight into the relationship between ERCC2 polymorphisms and glioma risk. A systematic literature search updated to December 2, 2013 was performed in the Pubmed and EMBASE databases. Crude pooled odds ratios (ORs) with their corresponding 95% confidence intervals (95% CIs) were used to estimate the association between ERCC2 polymorphisms and glioma risk under a suitable effect model according to heterogeneity. All analyses were performed using Review Manager 5 (version 5.2) and STATA (version 12.0). The combined results demonstrated rs13181 to be significantly associated with glioma risk (G allele versus T allele: OR=1.15, 95% CI=1.05-1.26, P=0.002; dominant model: OR=1.22, 95% CI=1.07-1.39, P=0.002; recessive model: OR=1.18, 95% CI=0.98-1.41, P=0.070). We also found that rs13181 acts in an allele dose-dependent manner (GG versus TT: OR=1.30, 95% CI=1.07-1.57, P=0.009; TG versus TT: OR=1.20, 95%=CI 1.05-1.37, P=0.009; trend test, P=0.004). However, no evidence was found in analyses for the association between other 3 ERCC2 polymorphisms (rs238406, rs1799793, and rs1052555) and susceptibility to glioma development. Our meta-analysis suggests that rs13181 is significantly associated with glioma risk in an allele dose-dependent manner, whereas, 3 other ERCC2 polymorphisms (rs238406, rs1799793, and rs1052555) may have no influence.

• Korean Journal of Radiology
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• v.23 no.11
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• pp.1078-1088
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• 2022

Objective: To develop and validate a model using radiomics features from apparent diffusion coefficient (ADC) map to diagnose local tumor recurrence in head and neck squamous cell carcinoma (HNSCC). Materials and Methods: This retrospective study included 285 patients (mean age ± standard deviation, 62 ± 12 years; 220 male, 77.2%), including 215 for training (n = 161) and internal validation (n = 54) and 70 others for external validation, with newly developed contrast-enhancing lesions at the primary cancer site on the surveillance MRI following definitive treatment of HNSCC between January 2014 and October 2019. Of the 215 and 70 patients, 127 and 34, respectively, had local tumor recurrence. Radiomics models using radiomics scores were created separately for T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CE-T1WI), and ADC maps using non-zero coefficients from the least absolute shrinkage and selection operator in the training set. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of each radiomics score and known clinical parameter (age, sex, and clinical stage) in the internal and external validation sets. Results: Five radiomics features from T2WI, six from CE-T1WI, and nine from ADC maps were selected and used to develop the respective radiomics models. The area under ROC curve (AUROC) of ADC radiomics score was 0.76 (95% confidence interval [CI], 0.62-0.89) and 0.77 (95% CI, 0.65-0.88) in the internal and external validation sets, respectively. These were significantly higher than the AUROC values of T2WI (0.53 [95% CI, 0.40-0.67], p = 0.006), CE-T1WI (0.53 [95% CI, 0.40-0.67], p = 0.012), and clinical parameters (0.53 [95% CI, 0.39-0.67], p = 0.021) in the external validation set. Conclusion: The radiomics model using ADC maps exhibited higher diagnostic performance than those of the radiomics models using T2WI or CE-T1WI and clinical parameters in the diagnosis of local tumor recurrence in HNSCC following definitive treatment.

• Korean Journal of Radiology
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• v.21 no.8
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• pp.1007-1017
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• 2020

Objective: The purpose of our study was to investigate the predictive abilities of clinical and computed tomography (CT) features for outcome prediction in patients with coronavirus disease (COVID-19). Materials and Methods: The clinical and CT data of 238 patients with laboratory-confirmed COVID-19 in our two hospitals were retrospectively analyzed. One hundred sixty-six patients (103 males; age 43.8 ± 12.3 years) were allocated in the training cohort and 72 patients (38 males; age 45.1 ± 15.8 years) from another independent hospital were assigned in the validation cohort. The primary composite endpoint was admission to an intensive care unit, use of mechanical ventilation, or death. Univariate and multivariate Cox proportional hazard analyses were performed to identify independent predictors. A nomogram was constructed based on the combination of clinical and CT features, and its prognostic performance was externally tested in the validation group. The predictive value of the combined model was compared with models built on the clinical and radiological attributes alone. Results: Overall, 35 infected patients (21.1%) in the training cohort and 10 patients (13.9%) in the validation cohort experienced adverse outcomes. Underlying comorbidity (hazard ratio [HR], 3.35; 95% confidence interval [CI], 1.67-6.71; p < 0.001), lymphocyte count (HR, 0.12; 95% CI, 0.04-0.38; p < 0.001) and crazy-paving sign (HR, 2.15; 95% CI, 1.03-4.48; p = 0.042) were the independent factors. The nomogram displayed a concordance index (C-index) of 0.82 (95% CI, 0.76-0.88), and its prognostic value was confirmed in the validation cohort with a C-index of 0.89 (95% CI, 0.82-0.96). The combined model provided the best performance over the clinical or radiological model (p < 0.050). Conclusion: Underlying comorbidity, lymphocyte count and crazy-paving sign were independent predictors of adverse outcomes. The prognostic nomogram based on the combination of clinical and CT features could be a useful tool for predicting adverse outcomes of patients with COVID-19.

• Annals of Occupational and Environmental Medicine
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• v.35
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• pp.27.1-27.12
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• 2023

Background: Environmental exposure is characterized by low concentration, chronic, and complex exposure. Traditional epidemiological studies show limitations in reflecting these characteristics since they usually focus on a single or very limited number of exposure factors at a time. In this study, we adopted the methodology of environment-wide association study (EWAS) to figure out the association of human liver function with various environmentally hazardous substances. Methods: We analyzed 2,961 participants from the Korean National Environmental Health Survey Cycle 4 (2018-2020). Using generalized linear model (GLM) analysis, we analyzed the association of 72 variables with 3 liver function indices (aspartate aminotransferase [AST], alanine aminotransferase [ALT], and gamma glutamyl transferase [GGT]). Finally, we visualized our results with Manhattan plot. Results: In GLM analysis, perfluorooctanesulfonate were positively associated with ALT (odds ratio [OR]: 2.2; 95% confidence interval [CI]: 1.39-3.46; p_adjusted = 0.0147) and perfluorodecanoic acid showed positive association with GGT (OR: 2.73; 95% CI: 1.36-5.5; p_adjusted = 0.0256). Plasma mercury showed positive association with GGT (OR: 1.45; 95% CI: 1.14-1.84; p_adjusted = 0.0315). Using a plastic container while keeping food in the refrigerator was associated with elevated GGT compared to using a glass container (OR: 1.51; 95% CI: 1.16-1.95; p_adjusted = 0.0153). 2-ethyl-5-oxohexyl phthalate, showed a negative trend with all 3 indices, with AST (OR: 0.54; 95% CI: 0.39-0.73; p_adjusted = 0.00357), ALT (OR: 0.5; 95% CI: 0.34-0.75; p_adjusted = 0.036), GGT (OR: 0.55; 95% CI: 0.4-0.76; p_adjusted = 0.00697). Bisphenol S and frequent use of sunblock cream showed negative association with ALT (OR: 0.77; 95% CI: 0.66-0.89), and GGT (OR: 0.25; 95% CI: 0.11-0.55), respectively. Conclusions: We conducted an exploratory study on environmental exposure and human liver function. By using EWAS methodology, we identified 7 factors that could have potential association with liver function.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.101-110
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• 2003

This paper introduces an identification method for nonlinear models in the form of rule-based Fuzzy-Neural Networks (FNN). In this study, the development of the rule-based fuzzy neural networks focuses on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The FNN modeling and identification environment realizes parameter identification through synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a HCM (Hard C-Means) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the identification algorithm of a GA hybrid scheme. The proposed GA hybrid scheme effectively combines the GA with the improved com-plex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model having sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process, and NOx emission process data from gas turbine power plants).

• Korean Journal of Fisheries and Aquatic Sciences
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• v.40 no.4
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• pp.201-209
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• 2007

Line transect sampling in a sighting survey is one of most widely used methods for assessing animal abundance. This study applied distance data, collected from three sighting surveys using line transects for finless porpoise that were conducted in 2004 and 2005 off the west coast of Korea, to four models (hazard-rate, uniform, half-normal and exponential) that can use a variety of detection functions, g (x). The hazard-rate model, a derived model for the detection function, should have a shoulder condition chosen using the AIC (Akaike Information Criterion), as the most suitable model. However, it did not describe a shoulder shape for the value of g(x) near the track tine and underestimated g (x), just as the exponential model did. The hazard-rate model showed a bias toward overestimating the densities of finless porpoises with a higher coefficient of variation (CV) than the other models did. The uniform model underestimated the densities of finless porpoise but had the lowest CV. The half-normal model described a detection function with a shape similar to that of the uniform model. The half-normal model was robust for finless porpoise data and should be able to avoid density underestimation. The estimated abundance of finless porpoise was 3,602 individuals (95% CI=1,251-10,371) inshore in 2005 and 33,045 individuals (95% CI=24,274-44,985) offshore in 2004.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.12
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• pp.7243-7249
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• 2013

Numerous studies have been conducted regarding association between TNF-${\alpha}$ and oral cancer risk, but the results remain controversial. The present meta-analysis is performed to acquire a more precise estimation of relationships. Databases of Pubmed, the Cochrane library and the China National Knowledge Internet (CNKI) were retrieved until August 10, 2013. Pooled odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated with fixed- or random-effect models. The heterogeneity assumption was assessed by I-squared test. Among the eight included case-control studies, all were focused on TNF-${\alpha}$-308G>A and four also concerned the TNF-${\alpha}$-238G>A polymorphism. It was found that oral cancer risk were significant decreased with the TNF-${\alpha}$-308G>A polymorphism in the additive genetic model (GG vs. AA, OR=0.19, 95% CI: [0.04, 1.00], P=0.05, I2=68.9%) and the dominant genetic model (GG+GA vs. AA, OR=0.22, 95% CI: [0.06, 0.82], P=0.03, I2=52.4%); however, no significant association was observed in allele contrast (G vs. A, OR=0.70, 95% CI: [0.23, 2.16], P=0.54, I2=95.9%) and recessive genetic models (GG vs. GA+AA, OR=0.72, 95% CI: [0.33, 1.57], P=0.41, I2=93.1%). For the TNF-${\alpha}$-238G>A polymorphism, significant associations with oral cancer risk were found in the allele contrast (G vs. A, OR=2.75, 95% CI: [1.25, 6.04], P=0.01, I2=0.0%) and recessive genetic models (GG vs. GA+AA, OR=2.23, 95%CI: [1.18, 4.23], P=0.01, I2=0.0%). Conclusively, this meta-analysis indicates that TNF-${\alpha}$ polymorphisms may contribute to the risk of oral cancer. Allele G and the GG+GA genotype of TNF-${\alpha}$-308G>A may decrease the risk of oral cancer, while allele G and the GG genotype of TNF-${\alpha}$-238G>A may cause an increase.

• The Journal of Advanced Prosthodontics
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• v.5 no.4
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• pp.452-456
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• 2013

PURPOSE. The aim of this study was to evaluate the repeatability of the digitizing of silicon rubber impressions of abutment teeth by using a white light scanner and compare differences in repeatability between different abutment teeth types. MATERIALS AND METHODS. Silicon rubber impressions of a canine, premolar, and molar tooth were each digitized 8 times using a white light scanner, and 3D surface models were created using the point clouds. The size of any discrepancy between each model and the corresponding reference tooth were measured, and the distribution of these values was analyzed by an inspection software (PowerInspect 2012, Delcamplc., Birmingham, UK). Absolute values of discrepancies were analyzed by the Kruskal-Wallis test and multiple comparisons (${\alpha}$=.05). RESULTS. The discrepancy between the impressions for the canine, premolar, and molar teeth were $6.3{\mu}m$ (95% confidence interval [CI], 5.4-7.2), $6.4{\mu}m$ (95% CI, 5.3-7.6), and $8.9{\mu}m$ (95% CI, 8.2-9.5), respectively. The discrepancy of the molar tooth impression was significantly higher than that of other tooth types. The largest variation (as mean [SD]) in discrepancies was seen in the premolar tooth impression scans: $26.7{\mu}m$ (95% CI, 19.7-33.8); followed by canine and molar teeth impressions, $16.3{\mu}m$ (95% CI, 15.3- 17.3), and $14.0{\mu}m$ (95% CI, 12.3-15.7), respectively. CONCLUSION. The repeatability of the digitizing abutment teeth's silicon rubber impressions by using a white light scanner was improved compared to that with a laser scanner, showing only a low mean discrepancy between $6.3{\mu}m$ and $8.9{\mu}m$, which was in an clinically acceptable range. Premolar impression with a long and narrow shape showed a significantly larger discrepancy than canine and molar impressions. Further work is needed to increase the digitizing performance of the white light scanner for deep and slender impressions.

• Health Policy and Management
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• v.30 no.4
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• pp.493-504
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• 2020

Background: There have been many studies on the associations between body mass index (BMI) and cognitive function. However, no study has ever compared the associations across the methods of categorizing BMI. In this study, we aimed to fill the gap in the previous studies and examine whether the obesity paradox is valid in the risk of cognitive function. Methods: Of the 10,254 people aged 45 and older from the Korean Longitudinal Study of Ageing from 2006 to 2016, 8,970 people were finalized as the study population. The dependent variable was whether a person has a normal cognitive function or not, and the independent variables of interest were BMI categorized by the World Health Organization Western Pacific Regional Office (WHO-WPRO) method, the WHO method, and a 10-group method. Covariates included sociodemographic factors, health behavior factors, and health status factors. A generalized linear mixed model analysis with a logit link was used. Results: In the adjusted model with all covariates, first, in the case of BMI categories of the WHO-WPRO method, underweight (odds ratio [OR], 1.16; 95% confidence interval [CI], 1.15-1.17), overweight (OR, 1.36; 95% CI, 1.35-1.36), and obese (OR, 1.34; 95% CI, 1.33-1.34) groups were more likely to have a normal cognitive function than a normal-weight group. Next, in the case of BMI categories of the WHO method, compared to a normal-weight group, underweight (OR, 1.15; 95% CI, 1.14-1.16) and overweight (OR, 1.06; 95% CI, 1.06-1.07) groups were more likely to have a normal cognitive function; however, obese (OR, 0.62; 95% CI, 0.61-0.63) group was less likely to have it. Lastly, in the case of the 10-group method, as BMI increased, the likelihood to have a normal cognitive function changed like a wave, reaching a global top at group-7 (26.5 kg/㎡ ≤ BMI <28.0 kg/㎡). Conclusion: The associations between BMI and cognitive function differed according to how BMI was categorized among people aged 45 and older in Korea, which suggests that cognitive function may be positively associated with BMI in some categories of BMI but negatively in its other categories. Health policies to reduce cognitive impairment need to consider this association between BMI and cognitive function.

• Journal of Animal Science and Technology
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• v.66 no.4
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• pp.682-701
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• 2024

This study estimated the heritabilities (h²) and genetic and phenotypic correlations between reproductive traits, including calving interval (CI), age at first calving (AFC), gestation length (GL), number of artificial inseminations per conception (NAIPC), and carcass traits, including carcass weight (CWT), eye muscle area (EMA), backfat thickness (BF), and marbling score (MS) in Korean Hanwoo cows. In addition, the accuracy of genomic predictions of breeding values was evaluated by applying the genomic best linear unbiased prediction (GBLUP) and the weighted GBLUP (WGBLUP) method. The phenotypic data for reproductive and carcass traits were collected from 1,544 Hanwoo cows, and all animals were genotyped using Illumina Bovine 50K single nucleotide polymorphism (SNP) chip. The genetic parameters were estimated using a multi-trait animal model using the MTG2 program. The estimated h² for CI, AFC, GL, NAIPC, CWT, EMA, BF, and MS were 0.10, 0.13, 0.17, 0.11, 0.37, 0.35, 0.27, and 0.45, respectively, according to the GBLUP model. The GBLUP accuracy estimates ranged from 0.51 to 0.74, while the WGBLUP accuracy estimates for the traits under study ranged from 0.51 to 0.79. Strong and favorable genetic correlations were observed between GL and NAIPC (0.61), CWT and EMA (0.60), NAIPC and CWT (0.49), AFC and CWT (0.48), CI and GL (0.36), BF and MS (0.35), NAIPC and EMA (0.35), CI and BF (0.30), EMA and MS (0.28), CI and AFC (0.26), AFC and EMA (0.24), and AFC and BF (0.21). The present study identified low to moderate positive genetic correlations between reproductive and CWT traits, suggesting that a heavier body weight may lead to a longer CI, AFC, GL, and NAIPC. The moderately positive genetic correlation between CWT and AFC, and NAIPC, with a phenotypic correlation of nearly zero, suggesting that the genotype-environment interactions are more likely to be responsible for the phenotypic manifestation of these traits. As a result, the inclusion of these traits by breeders as selection criteria may present a good opportunity for developing a selection index to increase the response to the selection and identification of candidate animals, which can result in significantly increased profitability of production systems.