Showing papers in "Biomedical Research-tokyo in 2017"

A hybrid neural network-gray wolf optimization algorithm for melanoma detection

Abstract: Melanoma is one of the most dangerous tumors in the human kind cancers. Nonetheless, early detection of this cancer can help the doctors to cure it perfectly. In this paper, a new efficient method is proposed to detect the malignant melanoma images from the images. In the proposed method, a hybrid technique is utilized. We first eliminate the extra scales by using edge detection and smoothing. Afterwards, the main hybrid technique is applied to segment the cancer images. Finally by using the morphological operations, the extra information is eliminated and used to focus on the area which melanoma boundary potentially exists. Here, Gray Wolf Optimization algorithm is utilized to optimize an MLP neural Networks (ANN). Gray Wolf Optimization is a new evolutionary algorithm which recently introduced and has a good performance in some optimization problems. GWO is a derivative-free, Meta Heuristic algorithm, mimicking the ecological behaviour of colonizing weeds. Gray wolf optimization is a global search algorithm while gradient-based back propagation method is local search. In this proposed algorithm, Multi-Layer Perceptron Network (MLP) employs the problem's constraints and GWO algorithm tries to minimize the root mean square error. Experimental results show that the proposed method can develop the performance of the standard MLP algorithm significantly.

Extraction of breast border and removal of pectoral muscle in wavelet domain

Abstract: Extraction of the breast border and simultaneously exclusion of pectoral muscle are principal steps for diagnosing of breast cancer based on mammogram data. The objective of propose method is to classify the mediolateral oblique fragment of the pectoral muscle. The extraction of breast region is performed using the multilevel wavelet decomposition of mammogram images. Moreover, artifact suppression and pectoral muscle detection is carried out by morphological operator. The efficient extraction with higher accuracy is validated on the set of 322 digital images taken from MIAS dataset.

Possible involvement of mucosal-associated invariant T cells in the progression of inflammatory bowel diseases.

Abstract: Mucosal-associated invariant T (MAIT) cells are innate-like T cells involved in anti-bacterial immunity. Recent studies have demonstrated that MAIT cells might be implicated in inflammatory bowel diseases (IBDs), but their precise function in IBD remains to be elucidated. We investigated the possible involvement of MAIT cells in the immunopathogenesis of IBDs. Heparinized peripheral blood and biopsy specimens of the colon were collected from 25 patients with ulcerative colitis (UC), 15 patients with Crohn's disease (CD), and 19 heathy individuals. Lymphocytes were isolated from the blood and colon, and then MAIT cells were analyzed by flow cytometry. The frequency of MAIT cells was significantly lower in the blood of IBD patients compared to healthy donors and significantly higher in the inflamed colons compared to healthy colons (P = 0.001). Among the IBD patients, the frequency of MAIT cells in the blood and colon was correlated with disease activities. In vitro activated MAIT cells from IBD patients secreted significantly more tumor necrosis factor-α and interleukin-17 than those from healthy donors. These findings indicate that MAIT cells are activated in IBD patients, and their accumulation in the inflamed mucosa is correlated with disease activities.

An evolution based hybrid approach for heart diseases classification and associated risk factors identification

Abstract: With the advent of voluminous medical database, healthcare analytics in big data have become a major research area. Healthcare analytics are playing an important role in big data analysis issues by predicting valuable information through data mining and machine learning techniques. This prediction helps physicians in making right decisions for successful diagnosis and prognosis of various diseases. In this paper, an evolution based hybrid methodology is used to develop a healthcare analytic model exploiting data mining and machine learning algorithms Support Vector Machine (SVM), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The proposed model may assist physicians to diagnose various types of heart diseases and to identify the associated risk factors with high accuracy. The developed model is evaluated with the results reported by the literature algorithms in diagnosing heart diseases by taking the case study of Cleveland heart disease database. A great prospective of conducting this research is to diagnose any disease in less time with less number of factors or symptoms. The proposed healthcare analytic model is capable of reducing the search space significantly while analyzing the big data, therefore less number of computing resources will be consumed.

Synapse-specific effects of IL-1β on long-term potentiation in the mouse hippocampus.

Abstract: Interleukin-1β (IL-1β) is a key molecule in the inflammatory responses elicited during infection and injury. It exerts local effects on synaptic plasticity by binding to IL-1 receptors that are expressed at high levels in the hippocampus. We examined the effects of IL-1β on synaptic plasticity in different hippocampal regions in acute mouse brain slices by measuring long-term potentiation (LTP). IL-1β (1 ng/mL) was applied for 30 min before LTP was induced with high-frequency stimulation (HFS). LTP was significantly impaired by either IL-1β application to the Schaffer collateral-CA1 synapses or the associational/commissural (A/C) fiber-CA3 synapses, which are both dependent on N-methyl-D-aspartate (NMDA) receptor activation. However, mossy fiber-CA3 LTP, which is expressed presynaptically in an NMDA-independent manner, was not impaired by IL-1β. Our results demonstrate that IL-1β exerts variable effects on LTP at different kinds of synapses, indicating that IL-1β has synapse-specific effects on hippocampal synaptic plasticity.

Ear biometrics for human classification based on region features mining

Abstract: This paper presents an ear biometric approach to classify humans. Accordingly an improved local features extraction technique based on ear region features is proposed. Accordingly, ear image is segmented in to certain regions to extract eigenvector from all regions. The extracted features are normalized and fed to a trained neural network. To benchmark results, benchmark database from University of Science and Technology Beijing (USTB) is employed that have mutually exclusive sets for training and testing. Promising results are achieved that are comparable in the state of art. However, a few region features exhibited low accuracy that will be addressed in the subsequent research.

Dynamic particle swarm optimization for personalized recommender system based on electroencephalography feedback

Abstract: Rapid growth of recommender systems (RSs) had proved its potential in the generation of personalized recommendations in various application domains. Generally, RSs learn the user's preferences and interests to suggest relevant items to the users. RSs are widely employed in various domains such as movies, e-commerce, travel, etc. Due to rapid growth in travel applications, Travel Recommender Systems (TRSs) had received a significant attention from researchers. Though existing TRS help users as digital support assistants in the travel, still the TRSs faces huge barriers in understanding user interests based on user's current emotional context. In this paper, to generate effective personalized Point of Interest (POI) recommendations, we present a Dynamic Particle Swarm Optimization and Hierarchy Induced K-Means (DPSOHiK) approach for the better POI clustering through utilizing electroencephalography (EEG) feedback. The DPSOHiK approach, with its capabilities to adapt the changing attributes helps in the POI clustering process. The clustered POIs are utilized in the recommendation process and based on the user's personal preferences the POIs are ranked to meet the requirements of the user. We have experimentally evaluated our proposed recommendation approach to demonstrate the recommendation potential and compared the obtained results with the baseline approaches. The experimental results depict the importance of EEG feedback in the enhancement of recommendation accuracy and provide helpful insights to the researchers to utilize EEG in the RSs research and development.

3D bones segmentation based on CT images visualization

Abstract: Currently, in the medical imaging, equipment produces 3D results for better visualization and cure. Likewise, need of capable devices for representation of these 3D medicinal images is expanding as it is valuable to view human tissues or organs straightforwardly and is an immense support to medical staff. Consequently, 3D images recreation from 2D images has attracted several researchers. The CT images normally composed of bones soft tissue and background. This paper presents 3D segmentation of leg’s bones in Computed Tomography images (CT scan). The bone section is extracted from each 2D slice and is placed in the 3D space. Surface rendering is applied on 2D bone slices. The data is visualized via multi-planar reformatting, surface rendering, and hardware-accelerated volume rendering. At last, the paper presents an enhanced technique to reproduce 3D bone segmentation in medicinal images that exhibits promising outcomes as compared to the techniques reported in literature. The proposed technique involves contour extraction, image enhancement, segmentation, outlier reduction and 3D modelling.

Emotion analysis using SAM (Self-Assessment Manikin) scale

Abstract: Background: Emotions are collective functional behaviors and action dispositions which has significant effects in our perception, thinking and behavior. The aim of our study is to detect and recognize the human emotion using SAM (Self-Assessment Manikin) rating. The objective of this study is to find the emotions of south Indian subjects and the classical dancers in addition to determine whether the perceived emotions are the same. Methods: A strong stimulus is need for inducing an emotion. Hence International Affective Picture System (IAPS) developed by the National Institute of Mental Health Center for the Study of Emotion and Attention at the University of Florida was used in our study. Fifty five subjects participated in the experiment (20 Males, 20 Females, and 15 Classical dancers). In this present study, a distinct attempt has been made by including the classical dancers among the group of participants. The perceived emotions are recorded in three dimensional spaces (valence, arousal, and dominance domain) for the Indian subjects using Self-Assessment Manikin Scale (SAM). Results: The male perceived the pleasant pictures in valence space as pleased and in arousal space it was excited (rs=0.511) in dominance space as dependant (rs=0.301). while female (rs=0.405) and classical dancers (rs=0.551) perceived the pleasant pictures in valence space as pleasant and in arousal space it was excited, as well as in dominance space as dependant (rs=0.202), (rs=0.210). The unpleasant pictures were rated as unpleasant by all in valance space whereas in arousal space female and male rated in arousal space rated as was dull (rs=0.222) and classical dancers as wide awake (rs=0.480). Whereas female (rs=0.170) and classical dancers (rs=0.332) rated dependant in dominance space. The neutral pictures were perceived as neutral by males, females and classical dancers. Conclusion: As emotion is subjective and it varies across culture, the valence scoring is same for gender to pleasant, unpleasant and neutral stimuli. The variations were noted in arousal and dominance space. The study concludes classical dancers perceived emotion for all the three categories of stimulus (pleasant/unpleasant/neutral) better when compared with female and male.

Modelling the H1N1 influenza using mathematical and neural network approaches

Abstract: Infectious diseases are threatening the people’s life because it spreads easily and its impacts are more dangerous. Government should take a necessary action to control the spread of the diseases and implement the prevention policies to secure people. Agent based and compartment simulation models are used for the epidemic disease outbreak. Compartment models are better than the agent based model for quick estimating and require less computer resources. The compartment model is consists of ordinary differential equations, which is used to analyse people behaviours and the spread of the disease. The goal of this study is to develop a new SIR based model for epidemic diseases. A proposed model extended the idea of SELMAHRD model. The model idea is to combine SIjRS and SELMAHRD models. In addition, Multi-hidden layer neural network with non-multiplier is also used in this paper to learn the spread of disease of disease dynamics.

Comprehensive analysis of gene mutation and expression profiles in neuroendocrine carcinomas of the stomach.

Abstract: The gene mutation and expression profiles of gastric neuroendocrine carcinoma (NEC) have not been comprehensively determined. Here, we examined the gene mutation and expression profiles of NEC using whole exome sequencing (WES) and microarray analysis. Six patients with gastric NEC and 13 with gastric adenocarcinoma (GAD) were included in this study. Single nucleotide variants were compared and multivariate statistical investigation with orthogonal partial least squares discriminant analysis (OPLS-DA) was performed to compare the difference in expression profiles between NEC and GAD. NEC showed a significantly higher mutation rate than GAD and the percentage difference in the mutation pattern of NEC compared with GAD was 92.8%. OPLSDA clearly discriminated between NEC and GAD. We identified 35 genes, including CPLX2 (Complexin 2), which were expressed more strongly in NEC than in GAD, of which 14 were neural-related. Immunohistochemical analysis showed the strong expression of CPLX2 in all NECs, versus expression in only 2 of 13 GADs. Gastric NEC had a specific mutation pattern with a significantly higher gene mutation rate than GAD, and completely differed from GAD on the basis of gene expression profile. CPLX2 might be a potential novel biomarker for the diagnosis of NEC.

Relationship between the poor glycemic control and risk factors, life style and complications

Abstract: It has shown that the decrease of blood glucose levels in patient with diabetes mellitus decreases mortality and morbidity rates. Main purpose in diabetes is to achieve and prevent the glycemic control. We aimed to evaluate the relationship between poor glycemic control and metabolic parameters, individual life and complications. Seven hundred fifty seven patients with type II diabetes mellitus have evaluated with demographical characteristic, body mass index, abdominal circumferences, blood pressures, dietary compliances, physical exercise statuses and laboratory analysis; and the relationship of these parameteres were investigated. Poor glycemic control was found significantly associated with duration of diabetes, age of onset, family history, job status, educational status, antidiabetic drugs, body mass index, abdominal circumference, hypertension, lipid and fasting plasma glucose levels. There was a significant relationship between the glycemic control and dietary compliance, physical activity, self blood glucose monitoring and drug compliance. While there is a significant relationship between the poor glycemic control and nephropathy, retinopathy, neuropathy and cardiovascular diseases; no significant relationship was seen in the cerebrovascular diseases and arthropathy. We have pointed the relationship of glycemic control with sociodemographic, medical status, life style, lipid levels and complications. Better results can be obtained by eliminating the factors related to poor glycemic control.

Abnormality detection using weighed particle swarm optimization and smooth support vector machine

Abstract: In this paper, a new hybrid classification approach, which uses Weighted-Particle Swarm Optimization (WPSO) for data clustering in sequence with Smooth Support Vector Machine (SSVM) for classification is proposed. The performance of WPSO clustering is compared with K means and fuzzy methods using intercluster, intracluster and validity index. The accuracy of proposed WPSO-SSVM classification methodology are 83.76% for liver disorder, 98.42% for WBCD, 95.21% for mammographic mass data which are better than in existing literature.

In vitro and in vivo biological screening of kefiran polysaccharide produced by Lactobacillus kefiranofaciens

Abstract: Kefiran is a functional fermented milk product traditionally used for its beneficial probiotic properties. It exhibits antimicrobial, antioxidant, anti-inflammatory anticancer and different health promoting characteristics. Although kefiran showed potential effects against many cancer cell lines, little information is present in the literature on its effect against cervical and hepatocellular carcinoma as well as on zebrafish embryos. The study aimed at investigating the cytotoxicity (in human cervical and hepatocellular carcinoma cell lines) and developmental toxicity (in zebrafish embryos) of kefiran produced by the fermentation of Lactobacillus kefiranofaciens. Cervical and hepatocellular cancer cells were exposed to serial concentrations of kefiran to evaluate its cytotoxic activities. Further biological effects of kefiran on the mortality and developmental abnormalities of zebrafish embryos were investigated. Results showed that kefiran significantly affected the viability of both tested cancer cell lines in a dose-dependent manner with IC50 values of 358.8 ± 1.65 and 413.5 ± 1.05 μg/ml for HeLa and HepG2 cells, respectively. Furthermore, kefiran adversely affected the morphological characteristics of the cells. Kefiran extract was much safer for zebrafish embryos and no mortality was observed up to 100 μg/ml, whereas the LC50 value (≥ 279.76 μg/ml) was also very high. Moreover, no developmental toxicity was observed up to 100 μg/ml concentration. Conclusively, microbially-produced kefiran showed anticancer properties in two tested human cancer cells, while its safer profiles in animals (zebrafish embryos) poses it as potential anticancer agent which does not affect normal tissue growth.

Non-Newtonian and Newtonian blood flow in human aorta: A transient analysis

Abstract: Pulsatile blood flow in an aorta of normal subject is studied by Computational Fluid Dynamics (CFD) simulations. The main intention of this study is to determine the influence of the non-Newtonian nature of blood on a pulsatile flow through an aorta. The usual Newtonian model of blood viscosity and a non- Newtonian blood model are used to study the velocity distributions, wall pressure and wall shear stress in the aorta over the entire cardiac cycle. Realistic boundary conditions are applied at various branches of the aorta model. The difference between non-Newtonian and Newtonian blood flow models is investigated at four different time instants in the fifth cardiac cycle. This study revealed that, the overall velocity distributions and wall pressure distributions of the aorta for a non-Newtonian fluid model are similar to the same obtained from Newtonian fluid model but the non-Newtonian nature of blood caused a considerable increase in Wall Shear Stress (WSS) value. The maximum wall shear stress value in the aorta for Newtonian fluid model was 241.706 Pa and for non-Newtonian fluid model was 249.827 Pa. Based on the results; it is observed that the non-Newtonian nature of blood affects WSS value. Therefore, it is concluded that the non-Newtonian flow model for blood has to be considered for the flow simulation in aorta of normal subject.

Effective ECG beat classification using higher order statistic features and genetic feature selection

Abstract: One of the most significant indicators of heart disease is arrhythmia. Detection of arrhythmias plays an important role in the prediction of possible cardiac failure. This study aimed to find an efficient machine-learning method for arrhythmia classification by applying feature extraction, dimension reduction and classification techniques. The arrhythmia classification model evaluation was achieved in a three-step process. In the first step, the statistical and temporal features for one heartbeat were calculated. In the second, Genetic Algorithms (GAs), Independent Component Analysis (ICA) and Principal Component Analysis (PCA) were used for feature size reduction. In the last step, Decision Tree (DT), Support Vector Machine (SVM), Neural Network (NN) and K-Nearest Neighbour (K-NN) classification methods were employed for classification. The proposed classification scheme categorizes nine types of Electrocardiogram (ECG) beats. The experimental results were compared in terms of sensitivity, specificity and accuracy performance metrics. The K-NN classifier attained classification accuracy rates of 98.86% and 99.11% using PCA and ICA features. The SVM classifier achieved its best classification accuracy rate of 98.92% using statistical and temporal features. The K-NN classifier feeding genetic algorithm features achieved the highest classification accuracy, sensitivity, and specificity rates of 99.30%, 98.84% and 98.40%, respectively. The results demonstrated that the proposed approach had the ability to distinguish ECG arrhythmias with acceptable classification accuracy. Furthermore, the proposed approach can be used to support the cardiologist in the detection of cardiac disorders.

Prediction of heart disease using k-nearest neighbor and particle swarm optimization

Abstract: Heart disease commonly occurring disease and is the major cause of sudden death nowadays This disease attacks the persons instantly Most of the people do not aware of the symptoms of heart disease Timely attention and proper diagnosis of heart disease will reduce the mortality rate Medical data mining is to explore hidden pattern from the data sets Supervised algorithms are used for the early prediction of heart disease Nearest Neighbor (KNN) is the widely used lazy classification algorithm KNN is the most popular, effective and efficient algorithm used for pattern recognition Medical data sets contain a large number of features The Performance of the classifier will be reduced if the data sets contain noisy features Feature subset selection is proposed to solve this problem Feature selection will improve accuracy and reduces the running time Particle Swarm Optimization (PSO) is an Evolutionary Computation (EC) technique used for feature selection PSO are computationally inexpensive and converges quickly This paper investigates to apply KNN and PSO for prediction of heart disease Experimental results show that the algorithm performs very well with 100% accuracy with PSO as feature selection

CD44 variant 9 expression as a predictor for gastric cancer recurrence: immunohistochemical and metabolomic analysis of surgically resected tissues

Abstract: CD44 variant 9 (CD44v9) and the heavy chain of 4F2 cell-surface antigen (CD98hc) appear important for regulation of reactive oxygen species defence and tumor growth in gastric cancer. This study examined the roles of CD44v9 and CD98hc as markers of gastric cancer recurrence, and investigated associations with energy metabolism. We applied capillary electrophoresis time-of-flight mass spectrometry to metabolome profiling of gastric cancer specimens from 103 patients who underwent resection with no residual tumor or microscopic residual tumor, and compared metabolite levels to immunohistochemical staining for CD44v9 and CD98hc. Positive expression rates were 40.7% for CD44v9 and 42.7% for CD98hc. Various tumor characteristics were significantly associated with CD44v9 expression. Five-year recurrence-free survival rate was significantly lower for CD44v9-positive tumors (39.1%) than for CD44v9-negative tumors (73.5%; P < 0.0001), but no significant differences in recurrence-free survival were seen according to CD98hc expression. Uni- and multivariate analyses identified positive CD44v9 expression as an independent predictor of poorer recurrence-free survival. Metabolome analysis of 110 metabolites found that levels of glutathione disulfide were significantly lower and reduced glutathione (GSH)/ glutathione disulfide (GSSG) ratio was significantly higher in CD44v9-positive tumors than in CD44v9-negative tumors, suggesting that CD44v9 may enhance pentose phosphate pathway flux and maintain GSH levels in cancer cells.

Complexity analysis of EEG motor movement with eye open and close subjects using multiscale permutation entropy (MPE) technique

Abstract: Background: Biological systems exhibit non-linear and spatiotemporal dynamics and structures even at rest. Humans demonstrate a remarkable ability to generate accurate and appropriate motor behaviour under many different and often uncertain environmental conditions. There are many motor movement tasks like eye open and close conditions, hand movements, fist movement etc. Brain controls all motor movement tasks. Electroencephalography (EEG) is a technique used to quantify the dynamics of physiological systems using non-invasive physiological monitoring and clinical investigation. The mental simulation of motor related tasks such as opening and closing of eye, left and right fist and fingers and other motor executive brain regions are commonly cognitive nature of tasks requires analysis using EEG motor movements. Methods: To quantify and understand the dynamics of EEG motor movements tasks, we employed robust Multiscale Permutation Entropy (MPE) analysis technique to distinguish Eye Open (EO) and Eye-Closed (EC) conditions. Mann-Whitney rank test was used to find significant differences between the groups and result were considered statistically significant for p-values<0.05. The Receive Operator Curve (ROC) was also computed to find the degree of separation between the groups. Results: The finding reveals that that frontal electrodes (F2, F3, F4, F5, F6, F7, F8) and front polar electrodes gives the highest separations and significant results to distinguish the EEG Motor movements tasks between eye open and eye closed tasks. The parietal (P3, P4), occipital (O1, O2) and central (C3, C4) electrodes gives only significant results at various temporal scales. The extremely significant results were obtained at F5, Fp1 followed by F1, F4, Fp2, F6 and F7. It was also found that frontal electrodes give the highest significance results followed by parietal, occipital and central electrodes which imply that these regions accordingly will help to distinguish these conditions from EEG motor movement tasks. MPE give higher significance results and separation at all selected electrodes than MSE to discriminate the brain states during EC and EO during the motor movement/imaginary tasks.

Simplified real-time heartbeat detection in ballistocardiography using a dispersion-maximum method

Abstract: Ballistocardiography (BCG) enables the recording of heartbeat, respiration, and body movement data from an unconscious human subject In this paper, we propose a new heartbeat detection algorithm for calculating heart rate (HR) and heart rate variability (HRV) from the BCG signal The proposed algorithm consists of a moving dispersion calculation method to effectively highlight the respective heartbeat locations and an adaptive heartbeat peak detection method that can set a heartbeat detection window by automatically predicting the next heartbeat location To evaluate the proposed algorithm, we compared it with other reference algorithms using a filter, waveform analysis and envelope calculation of signal by setting the ECG lead I as the gold standard The heartbeat detection in BCG should be able to measure sensitively in the regions for lower and higher HR However, previous detection algorithms are optimized mainly in the region of HR range (60~90 bpm) without considering the HR range of lower (40~60 bpm) and higher (90~110 bpm) HR Therefore, we proposed an improved method in wide HR range that 40~110 bpm The proposed algorithm detected the heartbeat greater stability in varying and wider heartbeat intervals as comparing with other previous algorithms Our proposed algorithm achieved a relative accuracy of 9829% with a root mean square error (RMSE) of 183 bpm for HR, as well as coverage of 9763% and relative accuracy of 9436% for HRV And we obtained the root mean square (RMS) value of 167 for separated ranges in HR

Hertwig's epithelial root sheath cells contribute to formation of periodontal ligament through epithelial-mesenchymal transition by TGF-β.

Abstract: In tooth root development, periodontal ligament (PDL) and cementum are formed by the coordination with the fragmentation of Hertwig's epithelial root sheath (HERS) and the differentiation of dental follicle mesenchymal cells. However, the function of the dental epithelial cells after HERS fragmentation in the PDL is not fully understood. Here, we found that TGF-β regulated HERS fragmentation via epithelial-mesenchymal transition (EMT), and the fragmented epithelial cells differentiated into PDL fibroblastic cells with expressing of PDL extracellular matrix (ECM). In the histochemical analysis, TGF-β was expressed in odontoblast layer adjacent of HERS during root development. Periostin expression was detected around fragmented epithelial cells on the root surface, but not in HERS. In the experiment using an established mouse HERS cell line (HERS01a), TGF-β1 treatment decreased E-cadherin and relatively increased N-cadherin expression. TGF-β1 treatment in HERS01a induced further expression of important ECM proteins for acellular cementum and PDL development such as fibronectin and periostin. Taken together, activation of TGF-βsignaling induces HERS fragmentation through EMT and the fragmented HERS cells contribute to formation of PDL and acellular cementum through periostin and fibronectin expression.

Induction of neural crest cells from human dental pulp-derived induced pluripotent stem cells.

Abstract: We previously generated induced pluripotent stem (iPS) cells from human dental pulp cells of deciduous teeth. Neural crest cells (NCCs) play a vital role in the development of the oral and maxillofacial region. Therefore, NCCs represent a cell source for bone, cartilage, and tooth-related tissue engineering. In this study, we examined whether iPS cells are capable of differentiating into NCCs through modification of the human embryonic stem cell protocol. First, iPS cells were dissociated into single cells and then reaggregated in low-cell-adhesion plates with neural induction medium for 8 days in suspension culture to form neurospheres. The neurospheres were transferred to fibronectin-coated dishes and formed rosette structures. The migrated cells from the rosettes abundantly expressed NCC markers, as evidenced by real-time polymerase chain reaction, immunofluorescence, and flow cytometric analysis. Furthermore, the migrated cells exhibited the ability to differentiate into neural crest lineage cells in vitro. They also exhibited tissue-forming potential in vivo, differentiating into bone and cartilage. Collectively, the migrated cells had similar characteristics to those of NCCs. These results suggest that human dental pulp cell-derived iPS cells are capable of differentiating into NCCs. Therefore, iPS cell-derived NCCs represent cell sources for bone and cartilage tissue engineering.

Discriminative deep belief networks for microarray based cancer classification

Abstract: Accurate diagnosis of cancer is of great importance due to the global increase in new cancer cases. Cancer researches show that diagnosis by using microarray gene expression data is more effective compared to the traditional methods. This study presents an extensive evaluation of a variant of Deep Belief Networks - Discriminative Deep Belief Networks (DDBN) - in cancer data analysis. This new neural network architecture consists Restricted Boltzman Machines in each layer. The network is trained in two phases; in the first phase the network weights take their initial values by unsupervised greedy layer-wise technique, and in the second phase the values of the network weights are fine-tuned by back propagation algorithm. We included the test results of the model that is conducted over microarray gene expression data of laryngeal, bladder and colorectal cancer. High dimensionality and imbalanced class distribution are two main problems inherent in the gene expression data. To deal with them, two preprocessing steps are applied; Information Gain for selection of predictive genes, and Synthetic Minority Over-Sampling Technique for oversampling the minority class samples. All the results are compared with the corresponding results of Support Vector Machines which has previously been proved to be robust by machine learning studies. In terms of average values DDBN has outperformed SVM in all metrics with accuracy, sensitivity and specificity values of 0.933, 0.950 and 0.905, respectively.

A new method of automatic recognition for tuberculosis disease diagnosis using support vector machines.

Abstract: Tuberculosis is an infectious disease caused by a bacillus called Mycobacterium tuberculosis. It can lead to death in untreated and inappropriately treated patients particularly in countries with low income. Therefore, early diagnosis of the disease not only increases treatment success, but also reduces death rates. Today, due to high classification and diagnosis rates, specialist systems have become an important tool in diagnosis of the disease. In this study, support vector machines (SVM), which is a machine learning technique was used for preliminary diagnosis of tuberculosis disease for the first time. A recognition system that was developed with the properties included in patient reports obtained from a local hospital was tested for its performance. The results indicated performance of the designed system was quite successful and that it could be used in diagnosis of the disease. Obtained diagnostic results were compared with similar studies using different specialist systems on this disease, and it was observed that our results were better.

Biofabrication of Zinc oxide nanoparticles and their in-vitro cytotoxicity towards gastric cancer (MGC803) cell

Abstract: This present report describes the green approach for the preparation of zinc oxide nanoparticles (ZnO NPs) using Murraya keenigii leaf extract by an eco-friendly approach. The produced ZnO NPs were characterized by using techniques such as X-Ray Diffraction (XRD), Fourier Transform Infrared (FTIR), Ultra Violet-Visible (UV-Vis), Energy Dispersive Spectroscopy (EDS) and Transmission Electron Microscopy (TEM). TEM and Dynamic Light Scattering (DLS) results have confirmed the formation of spherical ZnO NPs with average size of 20 nm. Further, the cytotoxicity results of the synthesized ZnO NPs showed their excellent biocompatibility towards gastric cancer (MGC803) cell lines, extending their scope of applications in biomedicine.

Analysis and selection of risk assessment frameworks for cloud based enterprise applications

Abstract: A lot of enterprise applications are available for the end users to use in different domains including business, healthcare, industrial and manufacturing. In the advent of cloud computing, it is imperative for organizations to determine risks involved in adopting cloud-based solutions or applications to ensure enterprise interest. The problem is the risk assessment of enterprise applications from the context of business. It is essential to adapt the right risk assessment strategy to handle the security situation proactively. We analyze the different risk assessment frameworks which would help to evaluate frameworks against enterprise application such as business, medical, finance accounting applications. We have evaluated both CWRAF and CVSS as two predominant frameworks in the risk assessment process against three business applications. The results help identifies the appropriate approach and framework which should be used for risk assessment.

The protective effects of Morus nigra L. leaves on the kidney function tests and histological structures in streptozotocin-induced diabetic rats

Abstract: Background and Aim: Diabetes Mellitus (DM) is associated with increased oxidative stress and its related complications. The phenolic components of mulberry (Morus nigra L.) leave have antioxidant components that can modulate oxidative stress. In this study, the beneficial effects of mulberry leaves extract were assessed in diabetic nephropathy and liver cells damage. Materials and Methods: Diabetes induced by high-fat diet and injection of 35 mg/kgBW Streptozotocin (STZ). Forty-four male wistar rats were divided into four groups: healthy control, non-treated, glibenclamide-treated, and extract-treated. The extract-treated group was treated with mulberry leaf extract for 4 weeks. At the end of treatment, kidney, liver and blood samples were collected to assay the biochemical analysis including fasting blood glucose level, albumin, creatinine, urea and uric acid concentrations, white blood cells, hemoglobin, hematocrit and histological evaluation. Results: Fasting blood glucose, creatinine, urea and uric acid were significantly in low levels in extract-treated group compared with the non-treated diabetic rats (p<0.001, p=0.03, p=0.009, and p=0.002; respectively). White blood cells level was low level (p<0.001) and hemoglobin and hematocrit levels were higher in extract treated group (p<0.001 and p=0.01; respectively). Serum albumin level in extract-treated rats was significantly higher than untreated group (p<0.001). Evaluating the histopathology of kidney showed that glycogen accumulation, fatty degeneration, and lymphocyte infiltration in extract-treated group were mild; while they were moderate in non-treated group; moreover, liver tissue evaluation showed that the fatty degeneration in extracttreated rats was mild and the cytoplasm of hepatocytes was distended by smaller amount of fatty droplets. Conclusion: The antioxidant properties of Mulberry leaves inhibited kidney and liver damage in diabetic rats. These results can be a base to evaluate the effects of mulberry (Morus nigra L.) leaves extract in the management of hyperuricemia, nephropathy, and fatty degeneration in liver cells in diabetic human patients.

Halal food identification with neural assisted enhanced RFID antenna

Abstract: Halal food identification in Muslims community is of high worth. However, the process of identifying and verification of halal food is difficult and time consuming for traders to help Muslims particularly. Accordingly, traders need to develop suitable verification strategy/tool to ensure halal product available in the market. The current research proposed a model that employs Radio Frequency Identification (RFID) technology in collaboration with neural networks. The features of the food product are extracted with RFID passive tag that is fed to train ANN (back-propagation algorithm) to verify halal product in stores to assist traders, sellers, and consumers. Promising results are thus obtained based on real users evaluation in terms of usability, satisfaction, and efficiency.

ECG signal classification and parameter estimation using multiwavelet transform

Abstract: The electrocardiogram (ECG) shows the plot of the bio-potential generated by the activity of the heart and is used by physicians to predict and treat various cardio vascular diseases. The QRS detection is a very important step in ECG signal processing. The main parameters concerned with QRS detection are sensitivity, accuracy, positive prediction and detection error. The methods used to detect QRS complex in ECG signals are Pan Tompkins algorithm, derivative based QRS detection and wavelet transform based QRS detection. In this paper performance comparison of wavelet transform based QRS detection with Pan Tompkins algorithm and derivative based QRS detection is done based on the characteristics of sensitivity, positive prediction and detection error. The accuracy of the proposed methodology is 93.35% and the specificity is 90%.

The prevalence of symptoms experienced during menopause, influence of socio-demographic variables on symptoms and quality of life among women at Abha, Saudi Arabia

Abstract: Background: The cultural practice and lifestyle, conditioned by socio-demographic factors greatly influence the perception of symptoms during menopause and in turn may affect quality of life of these women. The objective of this study is to evaluate the prevalence of symptoms during menopause, to determine the influence of socio-demographic factors on these symptoms and ‘Quality of Life’ among women at Abha, Saudi Arabia. Methods: A cross-sectional study was conducted among 228 women attending the five Primary Health Care Clinics located at different regions of Abha; the study participants were grouped into three categories according to the menstrual status: premenopausal (45.6%), perimenopausal (28.1%), and postmenopausal (26.3%). The standardized, self-administered Menopause Rating Scale (MRS) and questionnaire for socio-demographic factors were used as research tool. The mean MRS score were compared in the three groups and relationship of socio-demographic factors with MRS scores and quality of life were studied. Results: Majority of the women complained of joint and muscular discomfort (96.1%), irritability (94.7%), anxiety (89.0%) and hot flushes and sweating (80.7%). The mean total score for MRS scale was 15.25 ± 6.01. The mean score was 6.36 ± 3.01 for somatic symptoms, 6.05 ± 2.54 for psychological symptoms and 2.84 ± 2.25 for urogenital symptoms. Marital status, lower education level, parity, lack of exercise and chronic disease status were significantly associated with higher MRS and poor quality of life. Conclusion: Our study reveals that women at Abha, Saudi Arabia have moderate MRS scores, reflecting moderately poor quality of life and ability to cope with this phase of transition in life.