Glutathione dynamics is a potential predictive and therapeutic trait for neoadjuvant chemotherapy response in bladder cancer.

Radical cystectomy with preoperative cisplatin-based neoadjuvant chemotherapy (NAC) is the standard care for muscle-invasive bladder cancers (MIBCs). However, the complete response rate to this modality remains relatively low, and current clinicopathologic and molecular classifications are inadequate to predict NAC response in patients with MIBC. Here, we demonstrate that dysregulation of the glutathione (GSH) pathway is fundamental for MIBC NAC resistance. Comprehensive analysis of the multicohort transcriptomes reveals that GSH metabolism and immune-response genes are enriched in NAC-resistant and NAC-sensitive MIBCs, respectively. A machine-learning-based tumor/stroma classifier is applied for high-throughput digitalized immunohistochemistry analysis, finding that GSH dynamics proteins, including glutaminase-1, are associated with NAC resistance. GSH dynamics is activated in cisplatin-resistant MIBC cells, and combination treatment with a GSH dynamics modulator and cisplatin significantly suppresses tumor growth in an orthotopic xenograft animal model. Collectively, these findings demonstrate the predictive and therapeutic values of GSH dynamics in determining the NAC response in MIBCs.

Rex: R-linked EXcel add-in for statistical analysis of medical and bioinformatics data.

BACKGROUND: Microsoft Excel has substantial functionalities for data management and analyses, and has been the most popular software in this field. However, in spite of Excel's user-friendly interface and functionality for data management, it provides very few functions for in-depth statistical analyses, which has limited its wider application for this purpose. OBJECTIVE: Here, we introduce Rex, an Excel add-in software implementing the powerful analytical and graphical functions of R within Excel. METHODS: Rex was implemented using three types of programming software: R, JavaScript, and Microsoft VB.Net. RESULTS: Rex provides a graphical user interface (GUI) through Excel, and statistical analysis can be conducted by pointing and clicking the menu without programming R. Rex covers a wide range of analyses from basic statistics to advanced analysis, including structural equation modeling, complex sampling design, and machine learning models, making it possible for researchers not skilled in using a command-line interface to conduct in-depth statistical analyses. Most Rex modules are available in a free version for non-commercial use, and it can be used for educational and public purposes. CONCLUSION: In this article, we introduce the framework and features of Rex with illustrative examples of its implementation.

Artificial neural network-based ground reaction force estimation and learning for dynamic-legged robot systems.

Legged robots have become popular in recent years due to their ability to locomote on rough terrains; these robots are able to walk on narrow stepping-stones, go upstairs, and explore soft ground such as sand. Ground reaction force (GRF) is the force exerted on the body by the ground when they are in contact. This is a key element and is widely used for programming the locomotion of the legged robots. Being capable of estimating the GRF is advantageous over measuring it with the actual sensor system. Estimating allows one to simplify the system, and it is meant to be capable of prediction, and so on. In this article, we present a neural network approach for GRF estimation for the legged robot system. In order to fundamentally study the GRF estimation of the robot leg, we demonstrate our approach for a single-legged robot with a degree of freedom (DoF) of two with hip and knee joints on a flat-surface. The first joint is directly driven from the actuator, and another joint is belt-pulley driven from the second actuator to take advantage of the long range of motion. The neural network is designed to estimate GRF without attaching force sensors such as load cells, and the encoder is the only sensor used for the estimation. We propose a two-staged multi-layer perceptron (MLP) solution based on supervised learning to estimate GRF in the physical-world. The first stage of the MLP model is trained using datasets from the simulation, enabling it to estimate the simulation-staged GRF. The second stage of the MLP model is trained in the physical world using the simulation-staged GRF obtained from the first stage MLP as the input. This approach enables the second stage MLP to bridge the simulation to the physical world. The root mean squared error (RMSE) is 0.9949 N on the validation datasets in the best case. The performance of the trained network is evaluated when the robot follows trajectories that are not used in training the two-stage GRF estimation network.

A model for population exposure to PM2.5: Identification of determinants for high population exposure in Seoul.

Outdoor concentrations of particulate matter with an aerodynamic diameter of <2.5 µm (PM2.5) are often used as a surrogate for population exposure to PM2.5 in epidemiological studies. However, people spend most of their daily activities indoors; therefore, the relationship between indoor and outdoor PM2.5 concentrations should be considered in the estimation of population exposure to PM2.5. In this study, a population exposure model was developed to predict seasonal population exposure to PM2.5 in Seoul, Korea. The input data for the population exposure model comprised 3984 time-location patterns, outdoor PM2.5 concentrations, and the microenvironment-to-outdoor PM2.5 concentrations in seven microenvironments. A probabilistic approach was used to develop the Korea simulation exposure model. The determinants for the population exposure group were identified using a multinomial logistic regression analysis. Population exposure to PM2.5 varied significantly among the three seasons (p < 0.01). The mean ± standard deviation of population exposures to PM2.5 was 21.3 ± 4.0 µg/m3 in summer, 9.8 ± 2.7 µg/m3 in autumn, and 29.9 ± 10.6 µg/m3 in winter. Exposure to PM2.5 higher than 35 µg/m3 mainly occurred in winter. Gender, age, working hours, and health condition were identified as significant determinants in the exposure groups. An "unhealthy" health condition was the most significant determinant. The high PM2.5 exposure group was characterized as a higher proportion of males of a lower age with longer working hours. The population exposure model for PM2.5 could be used to implement effective interventions and evaluate the effectiveness of control policies to reduce exposure.

Effect of 6p21 region on lung function is modified by smoking: a genome-wide interaction study.

Smoking is a major risk factor for chronic obstructive pulmonary disease (COPD); however, more than 25% of COPD patients are non-smokers, and gene-by-smoking interactions are expected to affect COPD onset. We aimed to identify the common genetic variants interacting with pack-years of smoking on FEV1/FVC ratios in individuals with normal lung function. A genome-wide interaction study (GWIS) on FEV1/FVC was performed for individuals with FEV1/FVC ratio ≥ 70 in the Korea Associated Resource cohort data, and significant SNPs were validated using data from two other Korean cohorts. The GWIS revealed that rs10947231 and rs8192575 met genome-wide significant levels; For [Formula: see text] the likelihood ratio (LR) test was conducted, and its P values, PLR, for rs10947231 and rs8192575 were 2.23 × 10-12 and 1.18 × 10-8, respectively. Interaction between rs8192575 and smoking is significantly replicated with two additional data (PINT = 0.0454, 0.0131). Expression quantitative trait loci, topologically associated domains, and PrediXcan analyses revealed that rs8192575 is significantly associated with AGER expression. SNPs on the 6p21 region are associated with FEV1/FVC, and the effect of smoking on FEV1/FVC differs among the associated genotypes.

Alternative methylation of intron motifs is associated with cancer-related gene expression in both canine mammary tumor and human breast cancer.

BACKGROUND: Canine mammary tumor (CMT) has long been considered as a good animal model for human breast cancer (HBC) due to their pathological and biological similarities. However, only a few aspects of the epigenome have been explored in both HBC and CMT. Moreover, DNA methylation studies have mainly been limited to the promoter regions of genes. RESULTS: Genome-wide methylation analysis was performed in CMT and adjacent normal tissues and focused on the intron regions as potential targets for epigenetic regulation. As expected, many tumor suppressors and oncogenes were identified. Of note, most cancer-associated biological processes were enriched in differentially methylated genes (DMGs) that included intron DMRs (differentially methylated regions). Interestingly, two PAX motifs, PAX5 (tumor suppressive) and PAX6 (oncogenic), were frequently found in hyper- and hypomethylated intron DMRs, respectively. Hypermethylation at the PAX5 motifs in the intron regions of CDH5 and LRIG1 genes were found to be anti-correlated with gene expression, while CDH2 and ADAM19 genes harboring hypomethylated PAX6 motifs in their intron region were upregulated. These results were validated from the specimens originally MBD-sequenced as well as additional clinical samples. We also comparatively investigated the intron methylation and downstream gene expression of these genes using human breast invasive carcinoma (BRCA) datasets in TCGA (The Cancer Genome Atlas) public database. Regional alteration of methylation was conserved in the corresponding intron regions and, consequently, gene expression was also altered in HBC. CONCLUSIONS: This study provides good evidence for the conservation of epigenetic regulation in CMT and HBC, and suggests that intronic methylation can be an important factor in better understanding gene regulation in both CMT and HBC.

A Between Ethnicities Comparison of Chronic Obstructive Pulmonary Disease Genetic Risk.

Heterogeneity of lung function levels and risk for developing chronic obstructive pulmonary disease (COPD) among people exposed to the same environmental risk factors, such as cigarette smoking, suggest an important role of genetic factors in COPD susceptibility. To investigate the possible role of different genetic factors in COPD susceptibility across ethnicities. We used a population-stratified analysis for: (i) identifying ethnic-specific genetic susceptibility loci, (ii) developing ethnic-specific polygenic risk prediction models using those SNPs, and (iii) validating the models with an independent dataset. We elucidated substantial differences in SNP heritability and susceptibility loci for the disease across ethnicities. Furthermore, the application of three ethnic-specific prediction models to an independent dataset showed that the best performance is achieved when the prediction model is applied to a dataset with the matched ethnic sample. Our study validates the necessity of considering ethnic differences in COPD risk; understanding these differences might help in preventing COPD and developing therapeutic strategies.

Consumer exposure and risk assessment to selected chemicals of mold stain remover use in Korea.

Mold stain remover (MSR) is used to clean mold and mildew spots from surfaces and contains a variety of chemical substances. In this study, we estimated the inhalation and dermal exposures associated with the use of trigger spray MSRs, and performed screening-level risk assessments for the use of this type of product in Korea. Inhalation and dermal exposures were estimated using exposure algorithms based on exposure factors obtained from a nationwide survey of 10,000 participants and chemical analyses of the four most popular trigger spray MSRs. The hazard quotients (HQs) for noncancer risk and excess cancer risk (ECR) were calculated for each chemical. The mean inhalation exposure estimates for formaldehyde, benzene, chloroform, and carbon tetrachloride were 6.9 × 10-7, 1.7 × 10-7, 5.4 × 10-6, and 2.7 × 10-5 mg/kg/day, respectively. Dermal exposures of the chemicals were 5.7-6.5 times higher than inhalation exposures. The HQs for total exposure were all below 1, which indicated little noncancer risk from the use of MSRs. The safe ECR value of 1 × 10-6, was exceed in one subject for inhalation exposure of benzene and four subjects for dermal exposure of formaldehyde, while 19.8% for dermal exposure of benzene were above this value. Therefore, use of trigger spray MSRs in Korea should require more detailed exposure and risk assessment, especially for benzene.

Effect of population stratification on SNP-by-environment interaction.

Proportions of false-positive rates in genome-wide association analysis are affected by population stratification, and if it is not correctly adjusted, the statistical analysis can produce the large false-negative finding. Therefore various approaches have been proposed to adjust such problems in genome-wide association studies. However, in spite of its importance, a few studies have been conducted in genome-wide single nucleotide polymorphism (SNP)-by-environment interaction studies. In this report, we illustrate in which scenarios can lead to the false-positive rates in association mapping and approach to maintaining the overall type-1 error rate.

BALLI: Bartlett-adjusted likelihood-based linear model approach for identifying differentially expressed genes with RNA-seq data.

BACKGROUND: Transcriptomic profiles can improve our understanding of the phenotypic molecular basis of biological research, and many statistical methods have been proposed to identify differentially expressed genes (DEGs) under two or more conditions with RNA-seq data. However, statistical analyses with RNA-seq data are often limited by small sample sizes, and global variance estimates of RNA expression levels have been utilized as prior distributions for gene-specific variance estimates, making it difficult to generalize the methods to more complicated settings. We herein proposed a Bartlett-Adjusted Likelihood-based LInear mixed model approach (BALLI) to analyze more complicated RNA-seq data. The proposed method estimates the technical and biological variances with a linear mixed-effects model, with and without adjusting small sample bias using Bartlkett's corrections. RESULTS: We conducted extensive simulations to compare the performance of BALLI with those of existing approaches (edgeR, DESeq2, and voom). Results from the simulation studies showed that BALLI correctly controlled the type-1 error rates at various nominal significance levels and produced better statistical power and precision estimates than those of other competing methods in various scenarios. Furthermore, BALLI was robust to variation of library size. It was also successfully applied to Holstein milk yield data, illustrating its practical value. CONCLUSIONS;: BALLI is statistically more efficient and valid than existing methods, and we conclude that it is useful for identifying DEGs in RNA-seq analysis.

Evaluation of a human glycated hemoglobin test in canine diabetes mellitus.

Glycated hemoglobin A1c (HbA1c) is widely used for monitoring and diagnosing human diabetes mellitus, but is rarely used in veterinary clinics. The goal of our study was to validate the commercial HbA1c testing system SD A1cCare analyzer (Bionote, Gyeoggi-do, South Korea) for use in dogs. Dogs were recruited with owner's consent. Diabetic status was determined based on clinical signs, fasting hyperglycemia, and glycosuria. Intra-assay precision and linearity were evaluated with EDTA, heparin, or citrate as anticoagulants, and had excellent precision with mean coefficients of variation (CVs) of 2.47%, 2.26%, and 1.92%, respectively. Diluted anticoagulated blood samples showed excellent linear relationships with R2 of 0.991, 0.996, and 0.994, respectively. Inter-assay precision revealed that the mean CV of the normal control was 2.18% and that of the high control was 2.01% (30 repeats). Observed total error of a normal control was 7.81%, and 6.12% for the high control. HbA1c level measured before and after removal of plasma and replacement by saline showed minimal interference by lipid contents ( p = 0.929). The HbA1c concentrations of diabetic dogs were significantly higher than those of non-diabetic dogs ( p < 0.001). HbA1c value >6.2% indicated canine diabetes through a classification and regression tree model. In most cases, fructosamine and HbA1c were highly correlated ( r = 0.674, p < 0.001). The HbA1c testing system could be a valuable testing system to evaluate canine diabetes mellitus, providing an alternative in-house option for use by veterinary clinicians.

Characterization of a High PM2.5 Exposure Group in Seoul Using the Korea Simulation Exposure Model for PM2.5 (KoSEM-PM) Based on Time⁻Activity Patterns and Microenvironmental Measurements.

The Korea Simulation Exposure Model for fine particulate matter (PM2.5) (KoSEM-PM) was developed to estimate population PM2.5 exposure in Korea. The data were acquired based on 59,945 min of the actual microenvironmental PM2.5 measurements and on the time⁻activity patterns of 8072 residents of Seoul. The aims of the study were to estimate daily PM2.5 exposure of Seoul population, and to determine the characteristics of a high exposure group. KoSEM-PM estimated population exposures by applying the PM2.5 distribution to the matching time⁻activity patterns at 10-min intervals. The mean personal PM2.5 exposure level of the surveyed subjects in Seoul was 26.0 ± 2.7 µg/m³ (range: 21.0⁻40.2 µg/m³) in summer. Factors significantly associated with high exposure included day of the week, age, industry sector, job type, and working hours. Individuals surveyed on Saturdays were more likely to be in the high exposure group than those surveyed on weekdays and Sundays. Younger, non-office-working individuals with longer working hours were more likely to be in the high exposure group. KoSEM-PM could be a useful tool to estimate population exposure levels to other region in Korea; to expand its use, microenvironmental measurements are required for other region in Korea.

Genome-wide assessment of gene-by-smoking interactions in COPD.

Cigarette smoke exposure is a major risk factor in chronic obstructive pulmonary disease (COPD) and its interactions with genetic variants could affect lung function. However, few gene-smoking interactions have been reported. In this report, we evaluated the effects of gene-smoking interactions on lung function using Korea Associated Resource (KARE) data with the spirometric variables-forced expiratory volume in 1 s (FEV1). We found that variations in FEV1 were different among smoking status. Thus, we considered a linear mixed model for association analysis under heteroscedasticity according to smoking status. We found a previously identified locus near SOX9 on chromosome 17 to be the most significant based on a joint test of the main and interaction effects of smoking. Smoking interactions were replicated with Gene-Environment of Interaction and phenotype (GENIE), Multi-Ethnic Study of Atherosclerosis-Lung (MESA-Lung), and COPDGene studies. We found that individuals with minor alleles, rs17765644, rs17178251, rs11870732, and rs4793541, tended to have lower FEV1 values, and lung function decreased much faster with age for smokers. There have been very few reports to replicate a common variant gene-smoking interaction, and our results revealed that statistical models for gene-smoking interaction analyses should be carefully selected.

Determination of secondhand smoke leakage from the smoking room of an Internet café.

Although Internet cafes have been designated as nonsmoking areas in Korea, smoke-free legislation has allowed the installation of indoor smoking rooms. The purposes of this study were to determine secondhand smoke (SHS) leakage from an Internet café smoking room and to identify factors associated with SHS leakage. PM2.5 (particulate matter with an aerodynamic diameter ≤2.5 µm) mass concentrations were measured simultaneously both inside and outside the door to the smoking room. During each measurement, a field technician observed how long the smoking room door was opened and closed, the direction of door opening, and the number of smokers. A multivariate linear regression model was used to identify the causality of SHS leakage from the smoking room. A time series of PM2.5 concentrations both inside and outside the door to the smoking room showed a similar trend. SHS leakage was significantly increased because of factors associated with the direction of the smoking room door being opened, the duration of how long the smoking room door was opened until it was closed, and the average PM2.5 concentration inside the smoking room when the door was opened. SHS leakage from inside the smoking room to outside the smoking room was evident especially when the smoking room door was opened. Since the smoking room is not effective in preventing SHS exposure, the smoking room should be removed from the facilities to protect citizens from SHS exposure through revision of the current legislation, which permits installation of a smoking room. IMPLICATIONS: This paper concerns secondhand smoke (SHS) leakage from indoor smoking room. Unlike previous studies, the authors statistically analyzed the causality of PM2.5 concentration leakage from a smoking room using time-series analysis. Since the authors selected the most common smoking room, the outcomes could be generalized. The study demonstrated that SHS leakage from smoking room and SHS leakage were clearly associated with door opening. The finding demonstrated ineffectiveness of smoking room to protect citizens and supports removal of indoor smoking room.