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Multivessel disease (MVD) is an independent risk factor for poor prognosis in acute myocardial infarction patients. Although several global risk scoring systems (RSS) are in use in clinical practice, there is no dedicated RSS for MVD in ST-segment elevation myocardial infarction (STEMI). The primary objective of this study is to develop a novel RSS to estimate the prognosis of patients with MVD in STEMI.We used the Korean Acute Myocardial Infarction Registry (KAMIR) to identify 2,030 STEMI patients with MVD who underwent appropriate percutaneous coronary intervention (PCI). Their data were analyzed to develop a new RSS. The prognostic power of this RSS was validated with 2,556 STEMI patients with MVD in the Korean Working Group on Myocardial Infarction Registry (KORMI).Six prognostic factors related to all-cause death in STEMI patients with MVD were age, serum creatinine, Killip Class, lower body weight, decrease in left ventricular ejection fraction, and history of cerebrovascular disease. The RSS for all-cause death was constructed using these risk factors and their statistical weight. The RSS had appropriate performance (c-index: 0.72) in the KORMI validation cohort.We developed a novel RSS that estimates all-cause death in the year following discharge for patients with MVD in STEMI appropriately treated by PCI. This novel RSS was transformed into a simple linear risk score to yield a simplified estimate prognosis of MVD among STEMI patients.

Hematopoietic cancer is the malignant transformation in immune system cells. This cancer usually occurs in areas such as bone marrow and lymph nodes, the hematopoietic organ, and is a frightening disease that collapses the immune system with its own mobile characteristics. Hematopoietic cancer is characterized by the cells that are expressed, which are usually difficult to detect in the hematopoiesis process. For this reason, we focused on the five subtypes of hematopoietic cancer and conducted a study on classifying by applying machine learning algorithms both contextual approach and non-contextual approach. First, we applied PCA approach for extracting suited feature for building classification model for subtype classification. And then, we used four machine learning classification algorithms (support vector machine, k-nearest neighbor, random forest, neural network) and synthetic minority oversampling technique for generating a model. As a result, most classifiers performed better when the oversampling technique was applied, and the best result was that oversampling applied random forest produced 95.24% classification performance.

DNA methylation is suitable for early detection and is also significant to a wide variety of cancer detection and treatment fields. Accurate prediction of survival in cancer patients remains a challenge due to the ever-increasing heterogeneity and complexity of cancer. Recently, many studies have shown to extract biologically relevant latent features to learn the complexity of cancer by taking advantage of deep learning. However, there is still a lack of a generalized accurate and end-to-end approach to training deep learning models for DNA methylation data. In this paper, we design an end-to-end supervised variational autoencoder (S-VAE) with a single optimizer, which is used to extract useful information from high-dimensional DNA methylation data to provide crucial insights toward accurate early diagnosis and cancer treatment. The S-VAE method jointly predicts cancer type by minimizing the classification loss and DNA methylation by minimizing the reconstruction loss. Experimental results show that the proposed S-VAE method performs best in the DNA methylation-based classification of the lung cancer datasets from The Cancer Genome Atlas (TCGA) data portal compared to some state-of-the-art methods.

Atrial Fibrillation AF reported as the most occurring heart arrhythmia. Steadfast detection of AF in ECG monitoring systems is considerable for early treatment and health risks reduction. Various ECG mining and analysis efforts have addressed a wide variety of technical issues. However, the morphological descriptors are changing along the time within the different patients. As a result, the classification model constructed using old training data is not accurate enough to detect AF. This paper presents an outstanding dynamic learning method to achieve better AF arrhythmia detection in real-time applications. The performance of our proposed technique showed 96.2%, 99.7%, and 99.4% for sensitivity, specificity, and overall accuracy, respectively. Accordingly, the proposed Cache learning method can be introduced to improve the performance of the AF intelligent detection systems.

Coronary heart disease (CHD) is one of the top causes of global mortality. It does not give any symptoms at the early stage, and most patients are diagnosed during periods of CHD exacerbation. If CHD reaches a hard condition, it will require advanced and long-term treatments. Moreover, the early detection of CHD is not easy; doctors make a decision based on many kinds of clinical tests. In recent years, machine learning-based approaches are widely used in the healthcare domain to predict disease. In this study, we have proposed a deep neural network (DNN) model with an attention layer that gives importance weight to the input of the output layer for the CHD risk prediction model. The proposed method has been evaluated by predicting CHD risk in the Korean population and compared with typical machine learning algorithms. As a result, the proposed attention-based DNN improved the performance of DNN and outperformed regular machine learning classifiers. The performance measurements include accuracy, precision, recall, f1-score, and AUC score reached 82.7818%, 86.6499%, 77.2704%, 81.6631%, and 82.5802%, respectively.

The World Health Organization (WHO) reported that diabetes is now one of the top ten causes of global mortality, and it is also highly ranked in Korea. The poor lifestyle, such as lack of physical activity, unhealthy diet, overweight, and tobacco usage, directly increases the risk of diabetes. It is asymptomatic at the early stage, and most patients may be diagnosed several years after onset. Over time, it can lead to cardiovascular diseases, kidney failure, blindness, and limb amputation. Nowadays, machine learning-based approaches can play an essential role in the healthcare domain for disease prediction. In this study, we have proposed a deep neural network (DNN) with a reconstruction error (RE)-based feature to predict diabetes. First, a deep autoencoder (DAE) model is used to calculate the RE-based new feature. Then, the DNN classifier learns from a combination of the new feature and other risk factors to predict diabetes. We have performed an experiment on the Korea National Health and Nutrition Examination Survey (KNHANES) dataset and compared with typical machine learning algorithms. As a result of our experiment, proposed RE-based DNN outperformed regular machine learning classifiers.

Smoking is one of the major public health issues, which has a significant impact on premature death. In recent years, numerous decision support systems have been developed to deal with smoking cessation based on machine learning methods. However, the inevitable class imbalance is considered a major challenge in deploying such systems. In this paper, we study an empirical comparison of machine learning techniques to deal with the class imbalance problem in the prediction of smoking cessation intervention among the Korean population. For the class imbalance problem, the objective of this paper is to improve the prediction performance based on the utilization of synthetic oversampling techniques, which we called the synthetic minority over-sampling technique (SMOTE) and an adaptive synthetic (ADASYN). This has been achieved by the experimental design, which comprises three components. First, the selection of the best representative features is performed in two phases: the lasso method and multicollinearity analysis. Second, generate the newly balanced data utilizing SMOTE and ADASYN technique. Third, machine learning classifiers are applied to construct the prediction models among all subjects and each gender. In order to justify the effectiveness of the prediction models, the f-score, type I error, type II error, balanced accuracy and geometric mean indices are used. Comprehensive analysis demonstrates that Gradient Boosting Trees (GBT), Random Forest (RF) and multilayer perceptron neural network (MLP) classifiers achieved the best performances in all subjects and each gender when SMOTE and ADASYN were utilized. The SMOTE with GBT and RF models also provide feature importance scores that enhance the interpretability of the decision-support system. In addition, it is proven that the presented synthetic oversampling techniques with machine learning models outperformed baseline models in smoking cessation prediction.

We report a phase I pharmacological study of an oral formulation of CKD‐516, a vascular‐disrupting agent, in patients with refractory solid tumors. Twenty‐seven patients (16 in the dose‐escalation cohort and 11 in the expansion cohort) received a single daily dose (5‐25 mg) of CKD‐516 five days per week. Nausea (67%) and diarrhea (63%) were the most common treatment‐related adverse events. The recommended phase II dose of oral CKD‐516 was 20 mg/d (15 mg/d with a body surface area (BSA)

Coronary heart disease (CHD) is one of the leading causes of death worldwide; if suffering from CHD and being in its end-stage, the most advanced treatments are required, such as heart surgery and heart transplant. Moreover, it is not easy to diagnose CHD at the earlier stage; hospitals diagnose it based on various types of medical tests. Thus, by predicting high-risk people who are to suffer from CHD, it is significant to reduce the risks of developing CHD. In recent years, some research works have been done using data mining to predict the risk of developing diseases based on medical tests. In this study, we have proposed a reconstruction error (RE) based deep neural networks (DNNs); this approach uses a deep autoencoder (AE) model for estimating RE. Initially, a training dataset is divided into two groups by their RE divergence on the deep AE model that learned from the whole training dataset. Next, two DNN classifiers are trained on each group of datasets separately by combining a RE based new feature with other risk factors to predict the risk of developing CHD. For creating the new feature, we use deep AE model that trained on the only high-risk dataset. We have performed an experiment to prove how the components of our proposed method work together more efficiently. As a result of our experiment, the performance measurements include accuracy, precision, recall, F-measure, and AUC score reached 86.3371%, 91.3716%, 82.9024%, 86.9148%, and 86.6568%, respectively. These results show that the proposed AE-DNNs outperformed regular machine learning-based classifiers for CHD risk prediction.

Background: With advances in next-generation sequencing technologies, the bisulfite conversion of genomic DNA followed by sequencing has become the predominant technique for quantifying genome-wide DNA methylation at single-base resolution. A large number of computational approaches are available in literature for identifying differentially methylated regions in bisulfite sequencing data, and more are being developed continuously. Results: Here, we focused on a comprehensive evaluation of commonly used differential methylation analysis methods and describe the potential strengths and limitations of each method. We found that there are large differences among methods, and no single method consistently ranked first in all benchmarking. Moreover, smoothing seemed not to improve the performance greatly, and a small number of replicates created more difficulties in the computational analysis of BS-seq data than low sequencing depth. Conclusions: Data analysis and interpretation should be performed with great care, especially when the number of replicates or sequencing depth is limited.

Lung adenocarcinoma is the leading cause of death among men and women with cancer worldwide. Here, we performed an analysis of Illumina HumanMethylation450K data from TCGA to identify DNA methylation markers for lung adenocarcinoma diagnosis. We examined the DNA methylation landscape of lung adenocarcinoma and investigated the relationship between DNA methylation and clinical features. We then extracted differentially methylated cytosines in CpG island promoter regions, and then adopted machine learning techniques to determine the final methylation markers. As a result, we identified three methylation subtypes of lung adenocarcinoma, and found that the methylation status was not significantly related with the prognosis of lung adenocarcinoma. We finally identified two novel lung adenocarcinoma methylation markers including cg14823851 (TBX4) and cg07792478 (MIR124-2) with the AUCs of 100%, 100%, 98.3%, and 100% on support vector machine, logistic regression, decision tree, and random forest, respectively. Overall, our study demonstrates the potential use of methylation markers in lung adenocarcinoma diagnosis and may boost the development of new epigenetic therapies.

According to the World Health Organization (WHO), smoking has generated a lot of diseases, and tobacco has been the biggest threat to human beings. The Republic of Korea government has implemented a policy to reduce damage from smoking since 1986. But almost 1 out of 5 Koreans still smoked in 2017 (21.2%). In this research, we collected datasets from the Korea Health and Nutrition Examination Survey (KNHANES) from 2013 to 2015 and used statistical methods to analyze the smoking patterns of smokers among adults. We used the chi-square test for 28 independent variables based on the before and after preparation of the dependent variables and evaluated the result based on the significance level getting from the statistical analysis program SPSS. In our result, the gender distribution was found to be 2,407 (84.4%) for males and 444 (15.6%) for females. The age range was 46.36 ± 15.13 and the range was from 31 to 61 years. There were more single smokers than married smokers, and the results were significant in this study. Rather, it was reported that anti-smoking policy at home was not relevant, and anti-smoking policy public places were statistically significant (p = 0.007). The results of this study suggest that many smokers should make a decision to quit smoking by providing a negative aspect of smoking as a significant factor related to the preparation stage of smoking cessation.

The diabetes mellitus is a kind of growing epidemic which leads to several complications in recent years. Therefore, the prevention of diabetes mellitus is an important public health topic in medical domain. In this study, we try to find the risk factors of diabetes mellitus for the prediction of diabetes mellitus by using the association rule mining. In our experiment, we used the complex sampling-based feature selection approach and correlation-based feature selection approach to extract relevant features about the diabetes mellitus and generated the rules based on the discovered risk factors. Then we evaluated the discovered rules based on the support and confidence, and found useful rules. We expect our results are helpful for predicting the diabetes mellitus more easily in real life.

Coronary heart disease (CHD) is one of the top causes of death globally; if suffering from CHD, long time permanent treatments are required. Furthermore, the early detection of CHD is not easy; doctors diagnose it based on many kinds of clinical tests. Therefore, it is effective to reduce the risks of developing CHD by predicting high-risk people who will suffer from CHD. The Framingham Risk Score (FRS) is a gender-specific algorithm used to estimate at 10-years CHD risk of an individual. However, FRS cannot well estimate risk in populations other than the US population. In this study, we have proposed a deep neural network (DNN); this approach has been compared with the FRS and data mining-based CHD risk prediction models in the Korean population. As a result of our experiment, models using data mining have given higher accuracy than FRS-based prediction. Moreover, the proposed DNN has shown the highest accuracy and area under the curve (AUC) score, 82.67%, and 82.64%, respectively.

The present study aimed to identify novel methylation markers of clear cell renal cell carcinoma (ccRCC) using microarray methylation analysis and evaluate their prognostic relevance in patient samples. To identify cancer‑specific methylated biomarkers, microarray profiling of ccRCC samples from our institute (n=12) and The Cancer Genome Atlas (TCGA) database (n=160) were utilized, and the prognostic relevance of candidate genes were investigated in another TCGA dataset (n=153). For validation, pyrosequencing analyses with ccRCC samples from our institute (n=164) and another (n=117) were performed and the potential clinical application of selected biomarkers was examined. We identified 22 CpG island loci that were commonly hypermethylated in ccRCC. Kaplan‑Meier analysis of TCGA data indicated that only 4/22 loci were significantly associated with disease progression. In the internal validation set, Kaplan‑Meier analysis revealed that hypermethylation of two loci, zinc finger protein 492 (ZNF492) and G protein‑coupled receptor 149 (GPR149), was significantly associated with shorter time‑to‑progression. Multivariate Cox regression models revealed that hypermethylation of ZNF492 [hazard ratio (HR), 5.44; P=0.001] and GPR149 (HR, 7.07; P

Background Little is known about epigenetic silencing of genes by promoter hypermethylation in renal cell carcinoma (RCC). The aim of this study was to identify prognostic methylation markers in surgically treated clear cell RCC (ccRCC). Methods Methylation patterns were assayed using the Infinium HumanMethylation450 BeadChip array on pairs of ccRCC and normal tissue from 12 patients. Using quantitative PSQ analysis, tumor-specific hypermethylated genes were validated in 25 independent cohorts and their clinical relevance was also verified in 152 independent cohorts. Results Using genome-wide methylation array, Zinc finger protein 278 (ZNF278), Family with sequence similarity 155 member A (FAM155A) and Dipeptidyl peptidase 6 (DPP6) were selected for tumor-specific hypermethylated genes in primary ccRCC. The promoter methylation of these genes occurred more frequently in ccRCC than normal kidney in independent validation cohort. The hypermethylation of three genes were associated with advanced tumor stage and high grade tumor in ccRCC. During median follow-up of 39.2 (interquartile range, 15.4–79.1) months, 22 (14.5%) patients experienced distant metastasis. Multivariate analysis identified the methylation status of these three genes, either alone, or in a combined risk score as an independent predictor of distant metastasis. Conclusion The promoter methylation of ZNF278, FAM155A and DPP6 genes are associated with aggressive tumor phenotype and early development of distant metastasis in patients with surgically treated ccRCC. These potential methylation markers, either alone, or in combination, could provide novel targets for development of individualized therapeutic and prevention regimens., Graphical Abstract

Recently, the clinical and healthcare recommending service is required in medical center for the clinical diagnosis and plan of treatment in connection with cardiovascula disease. We propose a method of the clinical and healthcare recommending service based on cardiovascula disease pattern analysis for medical treatment service. We use SVM(Support Vector Machine) to segment the clinical historical data, to join patients’ clinical test data with input vectors of multi-parametric features, cardiovascula disease code, input factors and finally forms clusters of the clinical historical data based on electronic medical record. Then, we make an application on the clinical and healthcare recommending service for cardiovascula disease treatment information of cardiovascula patients to reduce patients’ search effort to get the curing information and the diagnosis for recovering their health, to improve the accuracy for the clinical and healthcare recommending service. We carry out experiments with data set of medical center to measure its performance. We report some of the experimental results.

CKD-516 is a potent vascular disrupting agent (VDA), selectively acting on tumor vessels. A clinical study of CKD-516 combined with irinotecan is undergoing in colorectal cancer. There have been exponential gains in immune-oncology (I-O) in recent times through the development of immune checkpoint inhibitors (ICIs). ICIs demonstrated durable response and some patients achieved disease control for several years. However, there are still critical unmet medical needs for the combination therapies due to limited response rate of ICIs. The first step of the cancer-immunity cycle is dendritic cells (DCs) maturation which is necessary for the anticancer immunity. It is well known that mature DCs play critical roles in priming immune responses in cancer patients. We found that CKD-516 is able to induce DC maturation through Rho signaling pathway in DCs. A tumor suppressor gene, SMAD4 is associated with several cancers including stomach cancer, colorectal cancer, and pancreas cancer. The loss of SMAD4 is a poor prognostic biomarker in these cancer types and its mutation is related to poor clinical outcome on immunotherapies. A SMAD4-deficient mouse colorectal cancer cell line (3349LM) was primary cultured from a spontaneous intestinal adenocarcinoma formed in a Villin-Cre;Smad4(F/F);Trp53(F/F) mouse. CKD-516 showed a synergistic effect with anti-PD-1 antibody in 3349LM syngeneic model. In addition, 3349LM is also a MSS type cancer. Thus, CKD-516 displayed a therapeutic potential for several cancer patients who do not respond to immunotherapy. In summary, CKD-516 is a novel VDA with immune boosting effect. In SMAD4 deficient cancer model, CKD-516 has shown synergistic effects in combination with PD-1 antibody. Therefore, these data suggest that CKD-516 potentiates the anticancer activity of immunotherapy. Citation Format: Soo Jin Kim, Hark Kyun Kim, Keun Ho Ryu, Chung Il Hong. CKD-516, a novel vascular disrupting agent, enhances anticancer activity of anti-PD-1 antibody in SMAD4-deficient colon cancer model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3216.

Recent years, the diabetes mellitus is an important public health problem and has been the top 10 leading causes of death in lower-middle-income countries and upper-middle-income countries in the world. In this study, we tried to use a hybrid feature selection approach to find proper and optimal feature subsets to classify and predict the diabetes mellitus patients in Korea based on the data from Korea National Health and Nutrient Examination Survey (KNHANES). We used the information gain feature selection approach as the filter phase and used the support vector machine with sequential search method as the wrapper phase. To validate the efficiency of the proposed approach, we also compared our proposed approach with several popular feature selection approaches. The results showed that our proposed approach can significantly improve the classification accuracy and outperformed other feature selection approaches.

Hypertension and prehypertension are risk factors for cardiovascular diseases. However, the associations of both prehypertension and hypertension with anthropometry, blood parameters, and spirometry have not been investigated. The purpose of this study was to identify the risk factors for prehypertension and hypertension in middle-aged Korean adults and to study prediction models of prehypertension and hypertension combined with anthropometry, blood parameters, and spirometry. Binary logistic regression analysis was performed to assess the statistical significance of prehypertension and hypertension, and prediction models were developed using logistic regression, naïve Bayes, and decision trees. Among all risk factors for prehypertension, body mass index (BMI) was identified as the best indicator in both men [odds ratio (OR) = 1.429, 95% confidence interval (CI) = 1.304⁻1.462)] and women (OR = 1.428, 95% CI = 1.204⁻1.453). In contrast, among all risk factors for hypertension, BMI (OR = 1.993, 95% CI = 1.818⁻2.186) was found to be the best indicator in men, whereas the waist-to-height ratio (OR = 2.071, 95% CI = 1.884⁻2.276) was the best indicator in women. In the prehypertension prediction model, men exhibited an area under the receiver operating characteristic curve (AUC) of 0.635, and women exhibited a predictive power with an AUC of 0.777. In the hypertension prediction model, men exhibited an AUC of 0.700, and women exhibited an AUC of 0.845. This study proposes various risk factors for prehypertension and hypertension, and our findings can be used as a large-scale screening tool for controlling and managing hypertension.

Background In Korea, the breastfeeding (BF) rate of infants aged 6 months or more is drastically decreasing, and this phenomenon is particularly worrisome for the future health of the population. The present study aimed to identify an antenatal strategy for initiation and continuation of human BF, and to identify how Baby-Friendly Hospitals (BFHs) may positively influence the intention to breastfeed. Methods A total of 414 pregnant Korean antenatal women were surveyed using questionnaires to determine current knowledge of the benefits of human breast milk, whether they planned to breastfeed after delivery, to continue BF after reinstatement in the workforce, are willing to abide by rooming-in care for infants, and plan to give birth at BFHs. Results We found that planning room-in care, greater awareness of BF benefits for infant and mother, participation in antenatal education programs, and provision of BF facilities in the workplace were positively associated with plans for exclusive breastfeeding (EBF) and longer BF duration. The mothers who planned to give birth at BFHs also desired to breastfeed immediately after birth, implement in-room care, continue BF at their workplace, participate in antenatal BF educational programs, and were more aware of the benefits of BF. Conclusion If the beneficial effects of BFHs were well known to individuals, these would enhance the success rate of BF in Korea. Antenatal education and consequent acquisition of better knowledge of the benefits of BF are important for increasing the rate of BF practices., Graphical Abstract

Objectives New methods for obtaining appropriate information for users have been attempted with the development of information technology and the Internet. Among such methods, the demand for systems and services that can improve patient satisfaction has increased in hospital care environments. Methods In this paper, we proposed the Hospital Exam Reservation System (HERS), which uses the data mining method. First, we focused on carrying clinical exam data and finding the optimal schedule for generating rules using the multi-examination pattern-mining algorithm. Then, HERS was applied by a rule master and recommending system with an exam log. Finally, HERS was designed as a user-friendly interface. Results HERS has been applied at the National Cancer Center in Korea since June 2014. As the number of scheduled exams increased, the time required to schedule more than a single condition decreased (from 398.67% to 168.67% and from 448.49% to 188.49%; p < 0.0001). As the number of tests increased, the difference between HERS and non-HERS increased (from 0.18 days to 0.81 days). Conclusions It was possible to expand the efficiency of HERS studies using mining technology in not only exam reservations, but also the medical environment. The proposed system based on doctor prescription removes exams that were not executed in order to improve recommendation accuracy. In addition, we expect HERS to become an effective system in various medical environments.

There is growing interest in integrating electronic patient-reported outcome (PRO) measures into routine oncology practice for symptom monitoring. Here, we evaluated the feasibility and accessibility of electronic PRO measures using a smartphone (PRO-SMART) for cancer patients receiving routine chemotherapy. The proposed PRO-SMART application obtains daily personal health record (PHR) data from cancer patients via a smartphone. An analysis report of cumulative PHR data is provided to the clinician in a format suitable for upload to electronic medical records (EMRs). Cancer outpatients who had received at least two cycles of chemotherapy and who were scheduled for two more cycles were enrolled. Between February 2015 and December 2016, 111 patients were screened and 101 of these were included. One-hundred patients used PRO-SMART at least once and were included in the final analysis (90.1% overall accessibility among all screened patients). The number of symptomatic adverse events (AEs) related to chemotherapy recorded in EMRs (mean ± standard deviation [SD]) increased from 0.92 ± 0.80 to 2.26 ± 1.80 (P

In this work, we analyzed only the patients of the NSTEMI (non ST-segment elevation myocardial infarction) who arrived at the hospital within 12 h after symptoms started. Using NSTEMI follow-up data within, the characteristics of the clinical data, the risk factor, and the blood tested in the hospital visit were analyzed for MACE (major adverse cardiac events) patients. MACE includes cardiac death, MI (myocardial infarction), Re-PCI, and CABG (coronary artery bypass graft). As a result, from the NSTEMI patients which can be followed up for over 12 m, NT-ProBNP (p=0.014) and age (p=0.045) are found to be the independent risk factors related to MACE. Accordingly, they can be useful for the diagnosis and prognosis for NSTEMI patients as a biomarker.

DNA methylation patterns are associated with the development and prognosis of cancer. The aim of this study was to identify novel methylation markers for the prediction of patient outcomes using microarray analysis of DNA methylation and RNA expression patterns in samples from long-term follow-up patients with nonmuscle invasive bladder cancer (NMIBC). A total of 187 human bladder specimens were used for microarray array or pyrosequencing (PSQ) analyses: 6 normal controls (NC) and 181 NMIBC. Tumor-specific hypermethylated genes were selected from a data set comprising 24 matched microarray-based DNA methylation and gene expression profiles (6 controls and 18 NMIBC), and their clinical relevance was verified by quantitative PSQ analysis. The methylation status of Homeobox A9 (HOXA9), ISL LIM homeobox 1 (ISL1) and Aldehyde dehydrogenase 1 family, member A3 (ALDH1A3) was significantly associated with decreased gene expression levels and aggressive clinicopathological characteristics. Multivariate regression analyses showed that hypermethylation of these genes was an independent predictor of disease recurrence (HOXA9, ISL1 and ALDH1A3, either alone or in combination) and progression (ISL1 and ALDH1A3, either alone or in combination) (each p

The antibody to hepatitis B surface antigen (anti-HBs) seropositivity rate after 3 doses of hepatitis B virus (HBV) vaccination during infancy period is known to be higher than 90%. However, a considerable number of vaccines do not form protective anti-HBs or chronologic decrease of anti-HBs. We retrospectively collected data of HBV serologic test results in 20,738 individuals from 2000 to 2015. After exclusion criteria were applied, 19,072 individuals were included. We analyzed the anti-HBs seropositivity rate, anti-HBs disappearance rate, anti-HBs positive seroconversion rate after receiving a booster vaccine, and the difference in anti-HBs positivity between the 2 groups; group A (born before 2005, while both recombinant vaccines and plasma-derived vaccines were used) and group B (born after 2005, when only recombinant vaccines were used by national regulation). The anti-HBs seropositivity rate was 55.8%, but there was a significant difference in the rate of seropositivity for anti-HBs between the group A and B (53.0% vs. 78.1%, P < 0.001). There was no significant age-adjusted difference in the mean seropositivity rate between the 2 groups (P = 0.058). In addition, the anti-HBs positivity rate was significantly lower in the group A as compared with the group B during infancy (83.1% vs. 92.1%, P < 0.001). A total of 1,106 anti-HBs-positive subjects underwent serologic tests more than twice. Of these, 217 subjects (19.6%) showed anti-HBs disappearance. After booster vaccinations, 87.4% (83/95) achieved seroconversion from seronegative to seropositive. Our results highlight the importance of lifelong protection against HBV and the possible necessity of booster vaccination after adolescent period., Graphical Abstract

Kidney plays an important role in human bodies. It maintains homeostasis and removes some harmful substance by making and ejecting urine. Renal cell carcinoma, especially clear cell renal cell carcinoma (ccRCC), is the most common type of kidney disease that accounts for 2∼3% of human malignancies. Early diagnosis and accurate classification of ccRCC is an important factor to decrease the motility rate. In this paper, the classification methods combined with feature selection have been examined on ccRCC clinical dataset. The data were obtained from The Cancer Genome Atlas (TCGA) that offers various cancer datasets including clinical data. We implemented 7 feature selection methods to identify important clinical factors and applied those factors into 4 classification algorithms such as C4.5, support vector machines, artificial neural network and Bayesian network. The experimental results showed that the Bayesian network with top 15 features was the most suitable prediction model. Moreover, the sensitivity was increased by employing feature selection approaches for classification of ccRCC data.

Recently, the cardiovascular disease has increased by causes su ch as westernization dietary life, smoking, and obesity. In particular, the acute myocardial infarction (AMI) occupies 50% death rate in cardiovascular disease. Following this trend, the AMI has been carried out a research for discovery of risk factors based on national data. However, ther e is a lack of diagnosis minor suitable for Korean. The objective of this paper is to develop a classifier for short-term relapse mortality prediction of cardiovascular disease patient based on prognosis data which is supported by KAMIR(Korea Acute Myocardial Infarction). Through this study , we came to a conclusion that ANN is the most suitable method for predicting the short-term relapse mortality of patients who have ST-segment elevation myocardial infarction. Also, data set obtained by logistic regression analysis performed highly efficient performance than existing d ata set. So, it is expect to contribute to prognosis estimation through proper classificatio n of high-risk patients▸Keywords:Cardiovascular Disease, Acute Myocardial Infarction, D ata-mining

In this study, the synergistic effect of 6-[4-(1-cyclohexyl- 1H-tetrazol-5-yl) butoxy]-3,4-dihydro-2(1H )-quinolinone (cilostazol) and Ginkgo biloba extract (GbE) was examined in apolipoprotein E (ApoE) null mice. Co-treatment with GbE and cilostazol synergistically decreased reactive oxygen species (ROS) production in ApoE null mice fed a high-fat diet. Co-treatment resulted in a significantly decreased atherosclerotic lesion area compared to untreated ApoE mice. The inflammatory cytokines and adhesion molecules such as monocyte chemoattractant-1 (MCP-1), soluble vascular cell adhesion molecule-1 (sVCAM-1), and VCAM-1 which can initiate atherosclerosis were significantly reduced by the co-treatment of cilostazol with GbE. Further, the infiltration of macrophages into the intima was decreased by co-treatment. These results suggest that co-treatment of GbE with cilostazol has a more potent anti-atherosclerotic effect than treatment with cilostazol alone in hyperlipidemic ApoE null mice and could be a valuable therapeutic strategy for the treatment of atherosclerosis.

Nowadays, the clinical and healthcare recommending service is required in medical center for clinical diagnosis of cardiovascula disease, plan of medical treatment. We propose a method of the clinical and healthcare recommending service using cardiovascula disease pattern analysis for medical treatment information. We use SVM(Support Vector Machine) to segment the clinical historical data, to join patient’s clinical test data with input vectors of different features, cardiovascula disease code, input factors and finally forms clusters of the clinical historical data based on the electronic discharge summaries data. Then, we make an application on the clinical and healthcare recommending service for cardiovascula disease treatment information of cardiovascula patients to reduce patients’ search effort to get the information of curing procedure, the diagnosis for recovering their health and to improve the rate of accuracy for the clinical and healthcare recommending service. We carry out experiments with data set of medical center to measure its performance. We report some of the experimental results.

Sensor networks integrated with cloud computing platform provides a promising way to develop monitoring system for elderly people especially dementia patients who need particular care for their normal life. In our work, we aim to design a comprehensive, unobtrusive, real-time, low-cost but effective monitoring system to help caregivers for their daily healthcare work. The system design has been finished and the entire experimental environment including sensor network and cloud model has been set up in our lab to collect simulated data and one health care center to collect real data. Though the system has been partially implemented due to time limit, the experiment results are encouraging.

The general pharmacological properties of 2-amino-9-(3-acetoxymethyl-4-isopropoxycar-bonyloxybut-1-yl) purine (CAS 247081-81-8, SK 1899), a new potential antiviral agent, were investigated in mice, rats, guinea pigs, rabbits, and dogs. The oral administration of 50, 150, and 500 mg/kg of SK 1899 had no effects on the central nervous system except that it slightly increased the spontaneous locomotor activity in mice at a dose of 500 mg/kg. SK 1899 did not disturb either the spontaneous motility or contractor-induced contraction of the isolated organs such as guinea pig ileum, rat uterus, guinea pig vas deferens, and guinea pig trachea at concentrations up to 10 –4 mol/l. It slightly increased the contractile force in the isolated guinea pig atrium at a concentration of 10 –4 mol/1. Following intravenous infusion of 5, 15, and 50 mg/kg of SK 1899 to anesthetized dogs, it did not change the mean arterial pressure, heart rate, left ventricular systolic pressure (LVSP), and respiratory rate, while it slightly increased the left ventricular positive dP/dt max (LV + dP/dt max ) at a dose of 50 mg/kg. SK 1899 did not induce any significant changes in the intestinal charcoal meal transit in mice, basal gastric juice secretion in rats, and renal function in rats. It did not affect the blood coagulation system and phenolsulfonphthalein secretion in rats. These findings suggest that SK 1899 has a very low potential to induce any adverse pharmacological effects at the doses showing antiviral activity.

Environmental monitoring is required to understand the effects of various kinds of phenomena such as a flood, a typhoon, or a forest fire. To detect the environmental conditions in remote places, monitoring applications employ the sensor networks to detect conditions, context models to understand phenomena, and computing technology to process the large volumes of data. In this paper, we present an air pollution monitoring system to provide alarm messages about potentially dangerous areas with sensor data analysis. We design the data analysis steps to understand the detected air pollution regions and levels. The analyzed data is used to track the pollution and to give an alarm. This implemented monitoring system is used to mitigate the damages caused by air pollution.

Photoplethysmogram(PPG) is the method to obtain the biomedical signal using the linear relationships between the blood volume for changing the cardiac contraction and relaxation and the amount of light for absorbing the hemoglobin in the blood. In this paper, we proposed the analyzed results which show the heart rate variability and the distribution of heart rate for before and after using PPG. Moreover, this paper designed and implemented the system based on personal computer to predict cardiovascular disease in advance using the analyzed results for the autonomic balance from taking the spectral analysis of heart rate and the state of the blood vessel for analyzing APG(acceleration plethysmogram).

Ginkgo biloba (G. biloba) extract is a widely used phytomedicine for the oral treatment of peripheral vascular disease. Cilostazol is a synthetic antiplatelet and vasodilating agent for the treatment of intermittent claudication resulting from peripheral arterial disease. It is likely to use concomitantly G. biloba extract and cilostazol for the treatment of peripheral arterial disease, which raises a concern of increasing their adverse effects of herbal-drug interactions. To clarify any possible herbal-drug interaction between G. biloba extract and cilostazol, the effect of the G. biloba extract on the pharmacokinetics of cilostazol was investigated. As cilostazol is known to be eliminated mainly by cytochrome P450 (CYP)-mediated metabolism, we investigated the effects of G. biloba extract on the human CYP enzyme activities and the effect of G. biloba extract on the pharmacokinetics of cilostazol after co-administration of the two agents to male beagle dogs. The G. biloba extract inhibited more or less CYP2C8, CYP2C9, and CYP2C19 enzyme activities in the in vitro microsomal study with values of 30.8, 60.5, and , respectively. In the pharmacokinetic study, co-administration with the G. biloba extract had no significant effect on the pharmacokinetics of cilostazol in dogs, although CYP2C has been reported to be responsible for the metabolism of cilostazol. In conclusion, these results suggest that there may not be a pharmacokinetic interaction between G. biloba extract and cilostazol.

Thrombosis and thromboembolic occlusions of major and minor blood vessels are a major complication in various peripheral vascular diseases. Antiplatelet agents (APA), key tools in the treatment of atherothrombosis, therefore became a mainstay medication for a wide range of vascular diseases. Cilostazol and Ginkgo biloba extract (GB), commonly used remedies for peripheral arterial disease, inhibit platelet aggregation with distinct therapeutic mechanisms. In this study, we have investigated if GB can potentiate the antiplatelet effects of cilostazol to explore the utility of combination therapy of cilostazol and GB against peripheral occlusive vascular diseases. GB or cilostazol was evaluated alone or in combination for the antiplatelet activity using in vitro and in vivo models. In addition, potential bleeding side effect of the combinative therapy was assessed by measuring bleeding time, prothrombin time (PT) and activated partial thromboplastin time (aPTT) in vivo after oral administration. In in vitro assays using freshly isolated human platelets, the combination of cilostazol and GB showed superior inhibition of both the shear and the collagen-induced platelet aggregation to those of each drug alone. In accordance with these enhanced in vitro antiplatelet activities, the combinative therapy showed enhanced anti-thrombotic effects in in vivo pulmonary embolism model and arterial thrombosis model. In particular, the increase of survival rate in pulmonary embolism model by combination treatment of cilostazol (25 mg/kg) and GB (20 mg/kg) was higher more than two-fold of those of the respective drugs. Notably, the combination of cilostazol and GB did not show a significant effect on the bleeding time, PT and aPTT increase, suggesting that GB may potentiate the antiplatelet effect of cilostazol without the prolongation of bleeding time or coagulation time. With these studies, we suggest that combinative therapy of GB and cilostazol might offer enhanced anti-thrombotic efficacies without increasing side-effects.

In order to diagnose cardiovascular disease, we proposed EP-based(emerging pattern- based) classification technique using multi-parametric diagnosis indexes. We analyzed linear/nonlinear features of HRV for three recumbent postures and extracted four diagnosis indexes from ST-segments to apply the multi-parametric diagnosis indexes. In this paper, classification model using essential emerging patterns for diagnosing disease was applied. This classification technique discovers disease patterns of patient group and these emerging patterns are frequent in patients with cardiovascular disease but are not frequent in the normal group. To evaluate proposed classification algorithm, 120 patients with AP (angina pectrois), 13 patients with ACS(acute coronary syndrome) and 128 normal people data were used. As a result of classification, when multi-parametric indexes were used, the percent accuracy in classifying three groups was turned out to be about 88.3%.

In present study, we proposed not only a novel methodology useful in developing the various features of heart rate variability (HRV), but also a suitable prediction model to enhance the reliability of medical examinations and treatments for coronary artery disease. In order to develop the various features of HRV, we analyzed HRV for three recumbent postures. The interaction effects between the recumbent postures and groups of normal people and heart patients were observed based on linear and nonlinear features of HRV. Forty-three control subjects and 64 patients with coronary artery disease participated in this study. In order to extract various features, we tested five classification methods and evaluated performance of classifiers. As a result, SVM and CMAR (gave about 72---88% goodness of accuracy) outperformed the other classifiers.

DNA methylation is a frequent and early epigenetic event with potential application as a biomarker for cancer detection and an indicator of disease evolution. The aim of the present study was to identify novel methylation markers for the prediction of patient outcomes using microarray analysis of DNA methylation in samples from long-term follow-up patients with non-muscle invasive bladder cancer (NMIBC). Candidate methylation markers were selected from our previously published genome-wide methylation profiles. The clinical relevance of candidate methylation markers was determined by quantitative pyrosequencing analysis of 136 human bladder specimens (8 normal controls and 128 NMIBCs). The reversibility of DNA methylation was examined by 5-Aza-CdR treatment in human bladder cancer cell lines. The methylation patterns of candidate markers were significantly associated with aggressive clinicopathological features. In multivariate regression analysis, hypermethylation of radial spoke head 9 homolog (RSPH9) was an independent predictor of disease recurrence (hazard ratio, 3.02; P=0.001) and progression (hazard ratio, 8.25; P=0.028). The methylation level of RSPH9 decreased with 5-Aza-CdR treatment and progressively increased in its absence in bladder cancer cell lines. RSPH9 methylation is an independent prognostic indicator in NMIBC patients, and could be of value for the assessment of disease recurrence and progression and for clinical decision-making regarding treatment.

Clustering of web users is an important research field in web mining. Information of web user clusters have been wildly used in many applications, such as solution of website structure optimization, reconstruction of website and distribution of advertising business. In this paper, we convert web log data into a sparse matrix, and propose a novel approach to calculate influence degree of each web page for all web users to build a Matrix of Influence Degree (MID) according to the generated sparse matrix, we can cluster web users simply from the generated MID. In the experiments, the results show that our proposed approach is capable of being the basic of clustering web users in web log data.

Aims Suppression of the hypothalamic–pituitary–gonadal axis has been widely utilized for the management of gonadal-hormone-dependent diseases such as endometriosis. Efforts to develop orally available gonadotropin-releasing hormone (GnRH) antagonists for the treatment of gonadal-hormone-dependent diseases led to the discovery of SKI2670, a novel non-peptide GnRH antagonist. The present study was undertaken to pharmacologically characterize SKI2670 in vitro and in vivo . Main methods We measured binding affinity and antagonistic activity of SKI2670 for the GnRH receptors. Immediate suppression of gonadotropins by single dosing of SKI2670 was examined in castrated monkeys. Subsequently, influence on gonadal hormones by prolonged administration of SKI2670 was assessed in naive female monkeys. To investigate in vivo efficacy of SKI2670, regression of ectopic implants by repeated administration of SKI2670 was examined in a rat endometriosis model. Key findings SKI2670 is a potent functional antagonist for the human GnRH receptor, with subnanomolar binding affinity. In castrated monkeys, single administration of SKI2670 lowered serum luteinizing hormone (LH) levels stronger with longer duration when compared to elagolix at equivalent doses. Moreover, repeated dosing of SKI2670 suppressed serum levels of gonadotropins and gonadal hormones in intact female monkeys while elagolix suppressed serum LH levels only. Finally, it exhibited regressive effects on ectopic implants in a rat endometriosis model without bone loss. Significance Our findings demonstrate robust GnRH antagonistic efficacy of SKI2670 in animal models, suggesting that SKI2670-induced suppression of the hypothalamic–pituitary–gonadal axis may be beneficial for the treatment of gonadal-hormone-dependent diseases such as endometriosis in humans.

Association rule mining has been applied actively in order to discover the hidden factors in acute myocardial infarction. There has been minimal research regarding the prognosis factor of acute myocardial infarction, and several previous studies has some limitations which are generation of incorrect population and potential data bias. Thus, we suggest the generation of prognosis factor based on association rule mining for acute myocardial infarction with ST-segment elevation. In our experiments, we obtain high interestingness factor based on Korean acute myocardial infarction registry which is corrected by 51 participating hospitals since 2005. The interestingness of the factor is evaluated by confidence. It is expected to contribute to prognosis management by high reliability factor.

This paper proposes an efficient recommending method for computerized treatment plan of cancer patients in Korea using electronic discharge summary of EMR in HL7. In this paper, it is necessary for us to classify disease patterns in the medical historical record to join the electronic discharge summaries data in EMR to analyze the disease pattern with input vectors of different features, disease code, to build the medical treatment plan of cancer patients in Korea using recommending service in medical data sets, to reduce inpatients’ search effort to get the information of curing procedure, the diagnosis for recovering their health and to improve the rate of accuracy of recommending service. To verify improved performance, we make experiments with dataset collected in medical center.

International audience; We evaluated the effect of SKI3246, the 50 % ethanol extract of the rhizome of Atractylodes japonica, on visceral hypersensitivity, which is a major characteristic feature of IBS. We used various rat models of visceral hypersensitivity to assess the visceral pain responses to colorectal distension (CRD) in comparison with conventional IBS treatments. Oral administration of SKI3246 dose-dependently and significantly attenuated the abdominal withdrawal reflex (AWR) score in a model of acetic acid-induced visceral hypersensitivity. We also found that it reduced the number of abdominal contractions in response to CRD in a model of 2,4,6-trinitrobenzenesulfonic acid-induced visceral hypersensitivity, which was comparable to ramosetron or alosetron. Furthermore, treatment with SKI3246 also increased the pain threshold and abolished the elevated AWR scores to CRD in a rat model of neonatal maternal separation. We presumed that the modulation of the NK2 receptor is involved in the inhibitory activity of SKI3246 on the basis that it significantly inhibited the contraction of the distal colonic muscle induced by neurokinin A, the NK2 receptor agonist. The present results indicate that SKI3246 has the potential to be an effective therapeutic agent for IBS, especially insofar as it can relieve visceral hypersensitivity.