bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of 2-[18F]-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography in rat models with hepatocellular carcinoma with liver cirrhosis
Authors: Park S.I. , Lee J.H. , Ham H.J. , Jung Y.J. , Park M.S. , Lee J. , Maeng L.S. , Chung Y.A. , Jang K.S.
Published: August 2015

Abstract

Liver cirrhosis is a predominant risk factor for hepatocellular carcinoma (HCC). However, the exact mechanism of the progression from cirrhosis to cancer remains unclear. The uptake of 2-[18F]-fluoro-2-deoxy-D-glucose (18F-FDG) is widely used as a marker of increased glucose metabolism to monitor the progression of cancer with positron emission tomography (PET)/computed tomography (CT). Here we investigated the feasibility of using 18F-FDG PET/CT in the diethylnitrosamine (DEN) mediated experimental hepatocellular carcinoma model. Rats received weekly intraperitoneal injections of DEN for 16 weeks for induction of HCC. We recorded starting from 0 days or 0 weeks after the last DEN injection. The weight and survival rate of rats were then measured. Also, an 18F-FDG PET scan and serum analysis were performed at minus 2, 0, plus 2, and plus 4 weeks after the last DEN injection. The body weight of rats was maintained between 350 g and 370 g during 14 and 20 weeks, and the rats were euthanized at 35 days after the last DEN injection. The serum levels of alanine transaminase (ALT), aspartate transaminase (AST), and alkaline phosphate (ALP) were significantly higher at zero weeks after the last DEN injection. The 18F-FDG uptake for the quantitative evaluation of HCC was done by measuring the region of interest (ROI). At minus two weeks after the last DEN injection, the ROI of rats had significantly increased compared to the normal group, in a time-dependent manner. These results suggest that FDG uptake serves as a good screening test to evaluate the feasibility of DEN-induced HCC.
In vivo evaluating skin doses for lung cancer patients undergoing volumetric modulated arc therapy treatment
Authors: Tseng Hsien-Chun , Pan Lung-Kang , Chen Hsin-Yu , Liu Wen-Shan , Hsu Chang-Chieh , Chen Chien-Yi
Published: August 2015

Abstract

This study is the first to use 10- to 90-kg tissue-equivalent phantoms as patient surrogates to measure peripheral skin doses (Dskin) in lung cancer treatment through Volumetric Modulated Arc Therapy of the Axesse linac. Five tissue-equivalent and Rando phantoms were used to simulate lung cancer patients using the thermoluminescent dosimetry (TLD-100H) approach. TLD-100H was calibrated using 6 MV photons coming from the Axesse linac. Then it was inserted into phantom positions that closely corresponded with the position of the represented organs and tissues. TLDs were measured using the Harshaw 3500 TLD reader. The ICRP 60 evaluated the mean Dskin to the lung cancer for 1 fraction (7 Gy) undergoing VMAT. The Dskin of these phantoms ranged from 0.51±0.08 (10-kg) to 0.22±0.03 (90-kg) mSv/Gy. Each experiment examined the relationship between the Dskin and the distance from the treatment field. These revealed strong variations in positions close to the tumor center. The correlation between Dskin and body weight was Dskin (mSv) = -0.0034x + 0.5296, where x was phantom’s weight in kg. R2 is equal to 0.9788. This equation can be used to derive an equation for lung cancer in males. Finally, the results are compared to other published research. These findings are pertinent to patients, physicians, radiologists, and the public.
A CT reconstruction approach from sparse projection with adaptive-weighted diagonal total-variation in biomedical application
Authors: Deng Luzhen , Mi Deling , He Peng , Feng Peng , Yu Pengwei , Chen Mianyi , Li Zhichao , Wang Jian , Wei Biao
Published: August 2015

Abstract

For lack of directivity in Total Variation (TV) which only uses x-coordinate and y-coordinate gradient transform as its sparse representation approach during the iteration process, this paper brought in Adaptive-weighted Diagonal Total Variation (AwDTV) that uses the diagonal direction gradient to constraint reconstructed image and adds associated weights which are expressed as an exponential function and can be adaptively adjusted by the local image-intensity diagonal gradient for the purpose of preserving the edge details, then using the steepest descent method to solve the optimization problem. Finally, we did two sets of numerical simulation and the results show that the proposed algorithm can reconstruct high-quality CT images from few-views projection, which has lower Root Mean Square Error (RMSE) and higher Universal Quality Index (UQI) than Algebraic Reconstruction Technique (ART) and TV-based reconstruction method.
Biological impedance cross evaluation and imaging from composite measurements of magnetic and electrical methods
Authors: Ran Peng , Xiao Xiaoming , He Wei , Li Zhangyong
Published: August 2015

Abstract

Because of the need for rapid detection and location of diseases in clinical applications, this work proposes a composite measurement of magnetic induction tomography (MIT) and electrical impedance tomography (EIT). This paper is composed of the following aspects: portable and integral hardware design, stable dual constant-current sources, the composite detection method, cross-plane data acquirement, 3-dimensional image reconstruction and so on. A qualitative evaluation of conductivity, resolution and relative position error were taken by combining the EIT and MIT methods via the experiment model. The sensitivities of both methods were analyzed to improve the imaging results. The reconstruction results reveal that the system is capable of obtaining better physiological measurements, which is very useful in clinical monitoring, quick medical diagnosing and preliminary screening of community health.
Sleep apnea classification based on respiration signals by using ensemble methods
Authors: Avcı Cafer , Akbaş Ahmet
Published: August 2015

Abstract

In this study, an efficient and robust method classifying the minute based occurrence of sleep apnea is aimed. Three respiration signals obtained from abdominal, chest and nasal way extracted from polysomnography recordings. Wavelet transform based on feature extraction methods are applied on the 1 minute length respiration signals. Dimension reduction process is facilitated by using principal component analysis. The features obtained from 8 recordings are used for the classification sleep apnea by using three ensemble classifiers. According to the results, the classification accuracies have been obtained between 92.07-98.43%, 92.75-98.68% and 92.42-98.61% by using three different ensemble classifier based on abdominal, chest and nasal based analysis, respectively for AdaBoost, Random Forest and Random Subspace. However the best result is obtained analyzing nasal based respiratory signal by using Random Forest method. In this case accuracy is 98.68%.
Application of an evolutionary algorithm in the optimal design of micro-sensor
Authors: Lu Qibing , Wang Pan , Guo Sihai , Sheng Buyun , Liu Xingxing , Fan Zhun
Published: August 2015

Abstract

This paper introduces an automatic bond graph design method based on genetic programming for the evolutionary design of micro-resonator. First, the system-level behavioral model is discussed, which based on genetic programming and bond graph. Then, the geometry parameters of components are automatically optimized, by using the genetic algorithm with constraints. To illustrate this approach, a typical device micro-resonator is designed as an example in biomedicine. This paper provides a new idea for the automatic optimization design of biomedical sensors by evolutionary calculation.
A portable wireless power transmission system for video capsule endoscopes
Authors: Shi Yu , Yan Guozheng , Zhu Bingquan , Liu Gang
Published: August 2015

Abstract

Wireless power transmission (WPT) technology can solve the energy shortage problem of the video capsule endoscope (VCE) powered by button batteries, but the fixed platform limited its clinical application. This paper presents a portable WPT system for VCE. Besides portability, power transfer efficiency and stability are considered as the main indexes of optimization design of the system, which consists of the transmitting coil structure, portable control box, operating frequency, magnetic core and winding of receiving coil. Upon the above principles, the correlation parameters are measured, compared and chosen. Finally, through experiments on the platform, the methods are tested and evaluated. In the gastrointestinal tract of small pig, the VCE is supplied with sufficient energy by the WPT system, and the energy conversion efficiency is 2.8%. The video obtained is clear with a resolution of 320×240 and a frame rate of 30 frames per second. The experiments verify the feasibility of design scheme, and further improvement direction is discussed.
Wireless charing pillow for a fully implantable hearing aid: Design of a circular array coil based on finite element analysis for reducing magnetic weak zones
Authors: Lim Hyung-Gyu , Kim Jong Hoon , Shin Dong Ho , Woo Seong Tak , Seong Ki Woong , Lee Jyung Hyun , Kim Myoung Nam , Wei Qun , Cho Jin-Ho
Published: August 2015

Abstract

Many types of fully implantable hearing aids have been developed. Most of these devices are implanted behind the ear. To maintain the implanted device for a long period of time, a rechargeable battery and wireless power transmission are used. Because inductive coupling is the most renowned method for wireless power transmission, many types of fully implantable hearing aids are transcutaneously powered using inductively coupled coils. Some patients with an implantable hearing aid require a method for conveniently charging their hearing aid while they are resting or sleeping. To address this need, a wireless charging pillow has been developed that employs a circular array coil as one of its primary parts. In this device, all primary coils are simultaneously driven to maintain an effective charging area regardless of head motion. In this case, however, there may be a magnetic weak zone that cannot be charged at the specific secondary coil’s location on the array coil. In this study, assuming that a maximum charging distance is 4 cm, a circular array coil—serving as a primary part of the charging pillow—was designed using finite element analysis. Based on experimental results, the proposed device can charge an implantable hearing aid without a magnetic weak zone within 4 cm of the perpendicular distance between the primary and secondary coils.
Sleep snoring detection using multi-layer neural networks
Authors: Nguyen Tan Loc , Won Yonggwan
Published: August 2015

Abstract

Snoring detection is important for diagnosing obstructive sleep apnea syndrome (OSAS) and other respiratory sleep disorders. In general, audio signal processing such as snoring sound analysis uses the frequency characteristics of the signal. Recently, a correlational filter Multilayer Perceptron neural network (f-MLP) has been proposed, which has the first hidden layer of correlational filter operations in frequency domain. It demonstrated a superior classification performance for the pattern sets; of these, frequency information is the dominant feature for classification. The first hidden layer is implemented with the correlational filter operation; its output is the power spectrum of the filter output, while the other layers are the same as the ordinary multilayer Perceptron (o-MLP). By using the back-propagation learning algorithm for the correlational filter layer, f-MLP was able to self-adapt the filter coefficients to produce its output with more discrimination power for classification in the higher layer. In this research, this f-MLP was applied for sleep snoring signal detection. As a result, the f-MLP achieved an average detection rate of 96% for the test patterns, compared to the conventional multilayer neural network that demonstrates an 82% average detection rate.
A method of extracting disease-related microRNAs through the propagation algorithm using the environmental factor based global miRNA network
Authors: Ha Jihwan , Kim Hyunjin , Yoon Youngmi , Park Sanghyun
Published: August 2015

Abstract

MicroRNAs (miRNA) are known to be involved in the development of various diseases. Hence various scientists in the field have been utilized computational analyses to determine the relationship between miRNA and diseases. However, the knowledge of miRNA and disease is still very limited. Therefore, we combined Environmental Factor (EF) data to a miRNA global network. Increasing research has shown that relationship between miRNAs and EFs play a significant role in classifying types of diseases. Environmental Factors consist of radiation, drugs, viruses, alcohol, cigarettes, and stress. Our global network considered all the interactions between every pair of miRNAs, which has led to precise analyses in comparison to local networks. As a result, our approaches’ performance demonstrated its effectiveness in identifying disease-related miRNA and this is the area under the ROC curve (AUC) of 74.46%. Furthermore, comparative experiment has shown that our approach performs comparable to other existing methods with an accuracy of 94%, 90% and 96% for breast cancer, colonic cancer, and lung cancer respectively. In conclusion, these results support that our research has broadened new biological insights on identifying disease-related miRNAs.
A comparative analysis of multiple sequence alignments for biological data
Authors: Manzoor Umar , Shahid Sarosh , Zafar Bassam
Published: August 2015

Abstract

Multiple sequence alignment plays a key role in the computational analysis of biological data. Different programs are developed to analyze the sequence similarity. This paper highlights the algorithmic techniques of the most popular multiple sequence alignment programs. These programs are then evaluated on the basis of execution time and scalability. The overall performance of these programs is assessed to highlight their strengths and weaknesses with reference to their algorithmic techniques. In terms of overall alignment quality, T-Coffee and Mafft attain the highest average scores, whereas K-align has the minimum computation time.
Faster single-end alignment generation utilizing multi-thread for BWA
Authors: Jo Heeseung , Koh Gunhwan
Published: August 2015

Abstract

Due to next-generation sequencing (NGS) technology, genome sequencing is able to process much more data at low cost. In NGS data analysis, the mapping of sequences into a reference genome takes the largest amount of time to process. Although the Burrows-Wheeler Aligner (BWA) tool is one of the most widely used open-source software tools to align read sequences, it is still limited in that it does not fully support multi-thread mechanisms during the alignment steps. In this paper, we propose a BWA-MT tool, evolved from BWA but supporting multi-thread computation, designed to fully utilize the underlying multi-core architecture of computing resources. By using multi-thread computation, BWA-MT can significantly shorten the time needed to generate an alignment for single-end read sequences. Meanwhile, it generates an identical Sequence Alignment Map (SAM) result file as BWA. To evaluate BWA-MT, we use an evaluation system equipped with twelve cores and 32 GB memory. As a workload, we used the hg19 human genome reference sequence and various numbers of read sequences from 1M to 40M. In our evaluation, BWA-MT displays up to 3.7 times faster performance and generates an identical SAM result file to BWA. Although the increased speed might be dependent on computing resources, we confirm that BWA-MT is highly efficient and effective.
Prediction model of sinoatrial node field potential using high order partial least squares
Authors: Feng Yu , Cao Hui , Zhang Yanbin
Published: August 2015

Abstract

High order partial least squares (HOPLS) is a novel data processing method. It is highly suitable for building prediction model which has tensor input and output. The objective of this study is to build a prediction model of the relationship between sinoatrial node field potential and high glucose using HOPLS. The three sub-signals of the sinoatrial node field potential made up the model’s input. The concentration and the actuation duration of high glucose made up the model’s output. The results showed that on the premise of predicting two dimensional variables, HOPLS had the same predictive ability and a lower dispersion degree compared with partial least squares (PLS).
Improve the diagnosis of atrial hypertrophy with the local discriminative support vector machine
Authors: Ling Ping , Gao Dajin , Zhou Xifeng , Huang Zhining , Rong Xiangsheng
Published: August 2015

Abstract

Computer-aided diagnosis (CAD) approaches succeed in detecting a number of diseases, however, they are not good at addressing atrial hypertrophy disease due to the lack of training data. Support Vector Machine (SVM) is very popular in few CAD solutions to atrial hypertrophy. Yet the performance of SVM is moderate in atrial hypertrophy detection compared to its success in other classification problems. In this paper we propose a novel CAD algorithm, Local Discriminative SVM (LDSVM), to overcome the above-mentioned difficulty. LDSVM consists of a global SVM that is trained on the training data, and a local kNN that is trained based on the information of SVM and query. When a query arrives, SVM gives the initial decision. If the initial decision is less confident, local kNN begins to modify the initial decision. LDSVM improves the accuracy of SVM through some contributions: the risk tube, the discriminant information derived from SVM hyperplane, the new metric and the self-tuning size of neighborhood. Empirical evidence on real medical datasets show high performance of LDSVM over the peers in addressing atrial hypertrophy problems.
HClass: Automatic classification tool for health pathologies using artificial intelligence techniques
Authors: Garcia-Chimeno Yolanda , Garcia-Zapirain Begonya
Published: August 2015

Abstract

The classification of subjects’ pathologies enables a rigorousness to be applied to the treatment of certain pathologies, as doctors on occasions play with so many variables that they can end up confusing some illnesses with others. Thanks to Machine Learning techniques applied to a health-record database, it is possible to make using our algorithm. hClass contains a non-linear classification of either a supervised, non-supervised or semi-supervised type. The machine is configured using other techniques such as validation of the set to be classified (cross-validation), reduction in features (PCA) and committees for assessing the various classifiers. The tool is easy to use, and the sample matrix and features that one wishes to classify, the number of iterations and the subjects who are going to be used to train the machine all need to be introduced as inputs. As a result, the success rate is shown either via a classifier or via a committee if one has been formed. A 90% success rate is obtained in the ADABoost classifier and 89.7% in the case of a committee (comprising three classifiers) when PCA is applied. This tool can be expanded to allow the user to totally characterise the classifiers by adjusting them to each classification use.
A novel fractal approach for predicting G-protein–coupled receptors and their subfamilies with support vector machines
Authors: Nie Guoping , Li Yong , Wang Feichi , Wang Siwen , Hu Xuehai
Published: August 2015

Abstract

G-protein-coupled receptors (GPCRs) are seven membrane-spanning proteins and regulate many important physiological processes, such as vision, neurotransmission, immune response and so on. GPCRs-related pathways are the targets of a large number of marketed drugs. Therefore, the design of a reliable computational model for predicting GPCRs from amino acid sequence has long been a significant biomedical problem. Chaos game representation (CGR) reveals the fractal patterns hidden in protein sequences, and then fractal dimension (FD) is an important feature of these highly irregular geometries with concise mathematical expression. Here, in order to extract important features from GPCR protein sequences, CGR algorithm, fractal dimension and amino acid composition (AAC) are employed to formulate the numerical features of protein samples. Four groups of features are considered, and each group is evaluated by support vector machine (SVM) and 10-fold cross-validation test. To test the performance of the present method, a new non-redundant dataset was built based on latest GPCRDB database. Comparing the results of numerical experiments, the group of combined features with AAC and FD gets the best result, the accuracy is 99.22% and Matthew’s correlation coefficient (MCC) is 0.9845 for identifying GPCRs from non-GPCRs. Moreover, if it is classified as a GPCR, it will be further put into the second level, which will classify a GPCR into one of the five main subfamilies. At this level, the group of combined features with AAC and FD also gets best accuracy 85.73%. Finally, the proposed predictor is also compared with existing methods and shows better performances.
Predicting a DNA-binding protein using random forest with multiple mathematical features
Authors: Guan Changge , Niu Xiaohui , Shi Feng , Yang Kun , Li Nana
Published: August 2015

Abstract

DNA-binding proteins are involved and play a crucial role in a lot of important biological processes. Hence, the identification of the DNA-binding proteins is a challenging and significant problem. In order to reveal the intrinsic information correlated to DNA-binding, nine classes of candidate features based on different mathematical fields are applied to construct the prediction model with random forest. They are fractal dimension, conjoint triad feature, Hilbert-Huang Transformation, amino acid composition, dipeptide composition, chaos game representation, and the corresponding information entropies. These mathematical expressions are evaluated with 5-fold cross validation test. The results of numerical simulations show that the mathematical features consisted of amino acid composition, fractal dimension and information entropies of amino acid and chaos game representation achieve the best performance. Its accuracy is 0.8157, and Matthew’s correlation coefficient (MCC) achieves 0.5968 on the benchmark dataset from DNA-Prot. By analyzing the components of top combination of the nine candidate features, the concepts of fractal dimension and information entropy are the effective and vital features, which can provide complementary sequence-order information on the basis of amino acid composition.
The study of threshold determination of gene identification and its improvement algorithms
Authors: Yan Chun , Liu Wei , Qiu Yiwei , Sun Haitang
Published: August 2015

Abstract

The problem of gene recognition based on the ratio of power spectrum, SNR, and Gabor transform and its implementation of the calculation were discussed. The optimal threshold could guarantee to identify the DNA sequences with the signal-to-noise ratio. It summarized three kinds of traditional ways to determine the threshold, and advanced the optimum entitled method showing the disparate degrees of highlight and the discrimination rate method of the exons or introns as far as possible to improve the rate of their accuracy. To evaluate different determination methods of threshold by using the calculation results of four kinds of DNA sequence. In order to ensure the analysis of DNA sequence more accurate, it adopted and improved gene identification method of Fourier transformation in a short time which is based on Gabor transformation. By using of the ergodic theory, the fixed percentage of the sequence length of exons in DNA has been improved to be the dynamic percentages which focus on different gene types. The exons of the DNA sequence which have been already discovered were identified by using the improved algorithm. With comparison of the results and the actual endpoint of exons, it confirmed that the improved algorithm can figure out the endpoint of the exons more accurate.
Integration of multi-microarray datasets to identify chronic obstructive pulmonary disease-related miRNAs
Authors: Hua Lin , Zheng Weiying , Xia Hong , Zhou Ping , An Li
Published: August 2015

Abstract

Currently, the mechanisms underlying chronic obstructive pulmonary disease (COPD) remain unclear. As potential biomarkers, microRNAs (miRNAs), which modulate the levels of specific genes and proteins, are important for enhancing our understanding of the mechanisms behind COPD. Although there have been a number of miRNA expression profiling analyses strategies used to document miRNA expression changes during physiological and pathological processes or used to identify differentially expressed miRNAs in disease or control samples, the study results have been inconsistently replicated using different datasets. For this reason, many findings cannot be well synthesized and interpreted. To address this issue, we used a multiple co-inertia analysis (MCIA) method to extract potential COPD-related miRNAs using three COPD microarray datasets. The results showed that miR-223, miR-132, and miR-199a-5p are obviously associated with COPD, and these results are consistent with the highly significant differentially-expressed miRNAs that were observed across three microarray datasets. Moreover, when miR-223, miR-132, and miR-199a-5p are taken as predictors to classify the samples of three datasets, the pooled sensitivity and specificity is 0.96 and 0.75, respectively, thereby suggesting that these three miRNAs can effectively distinguish COPD patients and controls.
Interactive Naive Bayesian network: A new approach of constructing gene-gene interaction network for cancer classification
Authors: Tian Xue W. , Lim Joon S.
Published: August 2015

Abstract

Naive Bayesian (NB) network classifier is a simple and well-known type of classifier, which can be easily induced from a DNA microarray data set. However, a strong conditional independence assumption of NB network sometimes can lead to weak classification performance. In this paper, we propose a new approach of interactive naive Bayesian (INB) network to weaken the conditional independence of NB network and classify cancers using DNA microarray data set. We selected the differently expressed genes (DEGs) to reduce the dimension of the microarray data set. Then, an interactive parent which has the biggest influence among all DEGs is searched for each DEG. And then we calculate a weight to represent the interactive relationship between a DEG and its parent. Finally, the gene-gene interaction network is constructed. We experimentally test the INB network in terms of classification accuracy using leukemia and colon DNA microarray data sets, then we compare it with the NB network. The INB network can get higher classification accuracies than NB network. And INB network can show the gene-gene interactions visually.
Simulation of the charge migration in DNA under irradiation with heavy ions
Authors: Belov Oleg V. , Boyda Denis L. , Plante Ianik , Shirmovsky Sergey Eh.
Published: August 2015

Abstract

A computer model to simulate the processes of charge injection and migration through DNA after irradiation by a heavy charged particle was developed. The most probable sites of charge injection were obtained by merging spatial models of short DNA sequence and a single 1 GeV/u iron particle track simulated by the code RITRACKS (Relativistic Ion Tracks). Charge migration was simulated by using a quantum-classical nonlinear model of the DNA–charge system. It was found that charge migration depends on the environmental conditions. The oxidative damage in DNA occurring during hole migration was simulated concurrently, which allowed the determination of probable locations of radiation-induced DNA lesions.
Control of HIV/AIDS infection system with drug dosages design via robust H∞ fuzzy controller
Authors: Assawinchaichote Wudhichai
Published: August 2015

Abstract

The designing of [Formula: see text] fuzzy controller for HIV/AIDS infected dynamic system has been considered in this paper. With TS fuzzy model and LMIs approach, the proposed controller is obtained for such a system. A set of sufficient conditions of the [Formula: see text] controller is given to ensure the closed-loop system asymptotic stability and the prescribed [Formula: see text] performance level. Finally, the effectiveness of the fuzzy controller design approach is finally presented through the simulation results.
Feature gene selection method based on logistic and correlation information entropy
Authors: Xu Jiucheng , Li Tao , Sun Lin
Published: August 2015

Abstract

In view of the characteristics of high dimension, small samples, nonlinearity and numeric type in the gene expression profile data, the logistic and the correlation information entropy are introduced into the feature gene selection. At first, the gene variable is screened preliminarily by logistic regression to obtain the genes that have a greater impact on the classification; then, the candidate features set is generated by deleting the unrelated features using Relief algorithm. On the basis of this, delete redundant features by using the correlation information entropy; finally, the feature gene subset is classified by using the classifier of support vector machine (SVM). Experimental results show that the proposed method can obtain smaller subset of genes and achieve higher recognition rate.
A label distance maximum-based classifier for multi-label learning
Authors: Liu Xiaoli , Bao Hang , Zhao Dazhe , Cao Peng
Published: August 2015

Abstract

Multi-label classification is useful in many bioinformatics tasks such as gene function prediction and protein site localization. This paper presents an improved neural network algorithm, Max Label Distance Back Propagation Algorithm for Multi-Label Classification. The method was formulated by modifying the total error function of the standard BP by adding a penalty term, which was realized by maximizing the distance between the positive and negative labels. Extensive experiments were conducted to compare this method against state-of-the-art multi-label methods on three popular bioinformatic benchmark datasets. The results illustrated that this proposed method is more effective for bioinformatic multi-label classification compared to commonly used techniques.
Gene environment interaction of GALNT2 and APOE gene with hypertension in the Chinese Han Population
Authors: Zhang Xuejuan , Zhao Haiping , Zhang Jietao , Han Di , Zheng Yu , Guo Xiaozi , He Dian , Guo Jin , Wang Yingcui
Published: August 2015

Abstract

In some GWAs studies, GALNT2 and APOE polymorphisms have been identified to be related to alterations of plasma or serum HDL-C and TG concentrations. The purpose of our study is to assess the contribution of GALNT2 rs4846914, APOE rs429358, rs7412, rs405509 variants, and several environmental factors to the development of hypertension disease in the China Han population. A hospital-based case-control study was conducted. Cases were hypertension ( n=211) and controls were normal participants ( n=434). The AA, AG, and GG genotype frequencies of GALNT2 rs4846914 were 22.8%, 43.1%, and 34.1% in hypertension subjects, and 35.3%, 44.2%, and 20.5% in controls ( P<0.05), respectively. The OR of the AG genotype adjusted for all risk factors compared to the AA genotype was 1.61 (95%CI: 1.02 to 2.56) and to the GG genotype 2.67 (95%CI: 1.59 to 4.488). There was no significant difference between the APOE rs429358, rs7412, and rs405509 genotype frequencies in hypertension and control subjects. The present work indicates that SNP rs4846914 in GALNT2 gene is related to an increased risk of hypertension in China Han population, but the APOE gene is not.
Integrative network analysis of rifampin-regulated miRNAs and their functions in human hepatocytes
Authors: Li Jin , Wang Ying , Wang Lei , Liang Hong , Feng Weixing , Meng Xianglian , Cong Wang , Liu Yunlong
Published: August 2015

Abstract

Rifampin is an important drug used in the treatment of tuberculosis, and it increases the drug metabolism in human hepatocytes. Previous studies have shown that rifampin can indirectly influence drug deposition through the regulation of molecular interactions of miRNA, PXR and other genes. The potential functions of miRNAs associated with rifampin- induced drug disposition are poorly understood. In this study, significantly differentially expressed miRNAs (SDEM) were extracted and used to predict the miRNA-regulated co-expression target genes (MCeTG). Additionally, a miRNA-regulated co-expressed protein interaction network (MCePIN) was constructed for SDEM by extending from the protein interaction network (PIN). The functioning of the miRNAs were analyzed using GO analysis and KEGG pathway enrichment analysis. A total of 20 miRNAs belonging to SDEM were identified, and 632 miRNA-regulated genes were predicted. The MCePIN was constructed by extending from PIN, and 10 miRNAs and 33 genes that are relevant to 7 functions, including response to wounding, wound healing, response to drug, defense response, inflammatory response, liver development and drug metabolism, were discerned. The results provided by this study offer valuable insights into the effect of rifampin on miRNAs, genes and protein levels.
Principal component-based feature selection for tumor classification
Authors: Sun Lin , Xu Jiucheng , Yin Ying
Published: August 2015

Abstract

One of the important problems in microarray gene expression data is tumor classification. This paper proposes a new feature selection method for tumor classification using gene expression data. In this method, three dimensionality reduction methods, including principal component analysis (PCA), factor analysis (FA) and independent component analysis (ICA), are first introduced to extract and select features for tumor classification, and their corresponding specific steps are given respectively. Then, the superiority of three algorithms is demonstrated by performing experimental comparisons on acute leukemia data sets. It is concluded that PCA compared with FA and ICA is the best under feature load ratio. However, PCA cannot make full use of the category information. To overcome the weak point, Fisher linear discriminant (FLD) is employed as those components of PCA, and a new approach to principal component discriminant analysis (PCDA) is proposed to retain all assets and work better than both PCA and FLD for classification. The further experimental results show that the classification ability of selected feature subsets by means of PCDA is higher than that of the other related dimensionality reduction methods, and the proposed algorithm is efficient and feasible for tumor classification.
Three-dimensional (3D) structure prediction and function analysis of the chitin-binding domain 3 protein HD73_3189 from Bacillus thuringiensis HD73
Authors: Zhan Yiling , Guo Shuyuan
Published: August 2015

Abstract

Bacillus thuringiensis (Bt) is capable of producing a chitin-binding protein believed to be functionally important to bacteria during the stationary phase of its growth cycle. In this paper, the chitin-binding domain 3 protein HD73_3189 from B. thuringiensis has been analyzed by computer technology. Primary and secondary structural analyses demonstrated that HD73_3189 is negatively charged and contains several α-helices, aperiodical coils and β-strands. Domain and motif analyses revealed that HD73_3189 contains a signal peptide, an N-terminal chitin binding 3 domains, two copies of a fibronectin-like domain 3 and a C-terminal carbohydrate binding domain classified as CBM_5_12. Moreover, analysis predicted the protein’s associated localization site to be the cell wall. Ligand site prediction determined that amino acid residues GLU-312, TRP-334, ILE-341 and VAL-382 exposed on the surface of the target protein exhibit polar interactions with the substrate.
Effects of hydrogen sulfide on myocardial fibrosis in diabetic rats: Changes in matrix metalloproteinases parameters
Authors: Xiao Ting , Zeng Ou , Luo Jian , Wu Zhixiong , Li Fang , Yang Jun
Published: August 2015

Abstract

In order to explore the effects of hydrogen sulfide (H2S) on myocardial fibrosis in diabetic rats and its underlying mechanisms, intraperitoneal injections of streptozotocin were used to establish the diabetes models and sodium hydrosulfide (NaHS) was used as an exogenous donor of H2S. Eight weeks later, Van Gieson staining was used to observe pathological changes, and basic hydrolysis methods were adopted to measure hydroxyproline content, while Western Blot was used to determine the expression of MMP2, MMP7, MMP11, MMP13, MMP16, TIMP1 and TGF β1.The results showed that significant myocardial fibrosis, decreased TIMP1 and MMP2 expression and increased MMP7, MMP11, MMP13, MMP16 expressions occurred in diabetic group, but all these changes were significantly reversed in diabetic rats after NaHS treatment. This suggests that H2S could attenuate cardiac fibrosis induced by diabetes and its mechanisms may be related to its modulation of MMPs/TIMPs expression and regulation of TGF β1.
The effect of different molecular weight collagen peptides on MC3T3-E1 cells differentiation
Authors: Liu Junli , Si Shaoyan , Qin Yaya , Zhang Bin , Song Shujun , Guo Yanchuan
Published: August 2015

Abstract

Our study aimed at investigating the effect of different molecular weight bovine collagen peptides, namely CH878, CH1370, CH2900, and CH7747 on the differentiation of MC3T3-E1 cells. Osteogenic differentiation of MC3T3-E1 cells was assessed by a series of specific assays, after culturing of cells in the presence of collagen peptides. Alkaline phosphatase activity (ALP) was evaluated by NBT/BCIP staining. Osteocalcin expression was determined by a radioimmunology method. Mineralization was quantified by Alizarin Red staining. ALP staining results demonstrated that the ALP staining of cells after culture in the presence of collagen peptides were significantly higher than the control group (P<0.05), indicating the promotion of ALP activity in MC3T3-E1 cells by these peptides. Radioimmunology results demonstrated that collagen peptides groups were all significantly higher than the control group (P<0.01). Alizarin Red staining results demonstrated that CH1370, CH2900, and CH7747 significantly promoted the formation of mineralized bone matrix. We therefore conclude that CH1370, CH2900, and CH7747 play an active role in the differentiation of MC3T3-E1 cells. Based on the above results, we provide molecular basis for further development of collagens with different molecular weight for the prevention and treatment of osteoporosis.
Abnormal increased re-released Hb from RBCs of an intrahepatic bile duct carcinoma patient was detected by electrophoresis release test
Authors: Su Yan , Han Lisha , Gao Lijun , Guo Jun , Sun Xiaorong , Li Jiaxin , Qin Wenbin
Published: August 2015

Abstract

In this paper, the hemoglobin (Hb) re-released from red blood cells (RBCs) and whole blood of 7 carcinoma patients were studied by using electrophoresis release test (ERT), which was established by our lab. Among the 7 carcinoma patients, the re-released Hb was distinctively increased from an intrahepatic bile duct carcinoma patient during one-dimension isotonic ERT. Different from the others, the result of double-dimension Hb re-release of this intrahepatic bile duct carcinoma patient showed that not only HbA but also HbA2 could be re-released from both RBCs and whole blood. The result of isotonic & hypotonic ERT which was performed at room temperature showed that more Hb could be re-released from both RBCs and whole blood of the intrahepatic bile duct carcinoma patient than that of the normal control. After keeping the samples at 37°C for 1 hour, the re-released Hb from RBCs could still be found more than that of the normal control, but was disappeared completely from the whole blood sample. To our surprise, when the isotonic & hypotonic ERT was repeated 2 days later at 37°C, the re-released Hb from RBCs of the intrahepatic bile duct carcinoma patient was increased only in tube 4-6, and disappeared in the other tube. Further mechanism research work cannot be continued because of the patient’s leave, but ERT is speculated to be a useful and effective technology to observe the physiological or pathological change of RBCs, blood or body in the future.
Potential mechanism of apoptosis induced by ultrasound in human hepatocarcinoma cells via comparative proteomic analysis
Authors: Feng Yi , Wan Mingxi
Published: August 2015

Abstract

To analyze the potential molecular mechanism of ultrasound induced apoptosis in cancer cells, comparative proteomic methods were introduced in the study. After ultrasound exposure at the intensity of 1.2 W/cm2, the human SMMC-7721 hepatocarcinoma cells were stained by trypan blue to detect the morphologic changes, and then the flow cytometry was used to examine the percentage of early apoptosis via double staining of FITC-labelled Annexin V and Propidium iodide. The proteins were separated by two-dimensional (2D) SDS polyacrylamide gel electrophoresis (PAGE). Among them, the differently expressed proteins were identified by MALDI-TOF mass spectrometry to reveal the key proteins response to ultrasound exposure. It’s proved early apoptosis of cells were induced by focused ultrasound. After ultrasound exposure, the expressing characteristics of several proteins changed, in which some proteins in HSP family are associated with apoptosis initiation. It is suggested that the focused ultrasound could be applied in the assistant cancer therapy. Moreover, it is proved the comparative proteomic methods could supply information about the protein expression to analyze the metabolic processes related to bio-effects of biomedical ultrasound.
Multiplex biomarker analysis biosensor for detection of hepatitis B virus
Authors: Xu Hua , Gu Dayong , He Jian’an , Shi Lei , Yao Jingyu , Liu Chunxiao , Zhao Chunzhong , Xu Yunqing , Jiang Shengyang , Long Jun
Published: August 2015

Abstract

In this paper, we report the development of a protein microarray-based biosensor for the detection of the hepatitis B virus (HBV) serological markers using surface plasmon resonance (SPR Printing buffer, protein immobilization time and concentration of the capture protein were optimized systematically to determine the best performance of the biosensor. Under optimal conditions, five hepatitis B markers in 20 μL human serum can be simultaneously detected within 30 minutes, whereas other methods such as ELISA and PCR can detect only one marker within four hours. This platform has been validated by analysis of 35 patients known to have hepatitis B, with 85% agreement between the test platform and analysis by commercial enzyme-linked immunosorbent assay (ELISA) kits. The results demonstrate that the protein microarray with SPR displayed a sensitivity of 0.1 ng mL−1 for HBsAg. In addition to high sensitivity, it also shows excellent specificity, reproducibility and stability. This integrated protein microarray technique combined with SPR is a promising candidate for hepatitis B diagnosis with high-throughput.
Development of biomarker positivity analysis system for cancer diagnosis based on clinical data
Authors: Yun Jung Min , Hwang Sung Jin , Anh Sung Yong , Lee Seung Min , Kang Peter , Lee Jeong Eun , Yoon Eun Sil , Choi Jin Wook , Park Sung-Hye , Seo Jung-Wook , Park Peom
Published: August 2015

Abstract

In Korea, there were 224,000 new cases of cancer and 75,334 deaths caused by cancer in 2013, which was three times more than the number of death caused by heart disease, the second leading cause of death. This study proposes a biomarker positivity analysis system based on clinical data, for personalized diagnosis and therapy of cancer. Data of 78,912 cases were obtained from immunopathology and surgical pathology reports. Data on sex, age, organ, diagnosis, and biomarkers were entered into a database. To verify the reliability of the clinical data, an additional 50,450 cases from positivity-related research papers were added. The proposed biomarker positivity analysis system makes it possible to extract and combine information for searching. The positivity values are in graphical and tabular format for ease of use. With a link to the internal network of the hospital, real-time pathology reports are available. Twenty-five pathology specialists are chosen as subjects to further confirm the reliability of this system; primary assessment results demonstrate a satisfaction level of 4.7 out of 5 and a concordance rate of 79% with positive data under the same conditions as reported in the literature. In the present study, analysis methods and platforms using large volumes of clinical and literature data are developed for cancer prognoses. It is expected that these tools will benefit both healthcare professionals and non-professionals involved in cancer diagnosis and treatment.
Milkvetch root improves immune function in patients with acute exacerbation of COPD
Authors: Jiang Donglin , Wang Xu , Su Qiang , Jiang Shengyang , Yuan Fenglai , Zhang Caidi , Gong Fang , Dong Qiaojing , Shi Jianping , Chen Baohua
Published: August 2015

Abstract

Milkvetch root as a medicine has been used for more over 2000 years in China, can strengthen immune function, protect liver, promote urination, resist aging and stress, reduce blood pressure and extensively resist bacterium. This study explored the effects of milkvetch root on the immune function of patients with a definitive diagnosis of acute exacerbation of chronic obstructive pulmonary disease (COPD). The patients were randomly assigned to either the experimental or control group. All patients received conventional clinical therapy; those in the experimental group were also administered milkvetch root. The serum levels of cytokines including tumor necrosis factor alpha (TNF- α), interleukin-8 (IL-8), IL-1 β, and IL-32 and immunocytes including T helper (Th), cytotoxic T (Tc), natural killer (NK), regulatory T (Treg) and B cells were measured 1 day before treatment and 7 and 14 days post-treatment. After bronchodilator inhalation, pulmonary function was evaluated at these same time points. The serum TNF- α, IL-8, IL-1 β, and IL-32 levels were significantly lower in the experimental group than in the control group 14 days post-treatment. The Th/Tc ratio and NK cell ratio was significantly higher but the Treg cell ratio was significantly lower in the experimental group than in the control group. The forced expiratory volume in 1 second (FEV1) and FEV1/forced vital capacity (FVC) were significantly higher in the experimental group than in the control group 14 days post-treatment. These results indicate that milkvetch root can improve the immune function of patients with acute exacerbation of COPD.
Protective effect of vitamin E against acute kidney injury
Authors: Liu Pengfei , Feng Yetong , Wang Yi , Zhou Yulai , Zhao Lei
Published: August 2015

Abstract

It has been well-known for many years now that vitamin E is an essential nutrient; however, some of the physiological functions of this vitamin are still far from being understood. In recent years, a series of preclinical and clinical studies proposed a protective role of vitamin E on acute kidney injury (AKI), which has a high morbidity rate and mortality rate in clinical investigations. Based on the benefits associated with vitamin E, such as strong antioxidant function, low toxicity, rare side-effects, and low cost, this therapy strategy has garnered an extensive amount of interest in the scientific community for the development of new therapy modes against AKI. In this review, a concise overview of the application of vitamin E in the treatment of AKI is provided as well as a summary of a series of published data regarding the combination therapy modes and detailed therapy mechanisms of vitamin E-based therapy against AKI. At present, there are critical points of this therapy mode that are still in need of further clarification, meaning the current understanding of the role of vitamin E in the treatment of AKI remains incomplete. However, the development of more reliable pharmacological or biotechnical strategies with vitamin E for the eventual treatment of patients with AKI may guide the next chapter of vitamin E research.