Evaluation of the anticonvulsant effect of carvacrol in Pentylenetetrazole (PTZ)-induced seizures in male mice: N-Methyl-D-Aspartic Acid receptor role

Background. Epilepsy is one of the most common neurological disorders after stroke. Due to the side effects and poor response of conventional anticonvulsant drugs, researchers have turned their attention to find new drugs. Carvacrol is a phenolic compound with neuroprotective, anti-inflammatory, antioxidant and anticonvulsant effects. The aim of the study was to investigate the anticonvulsant effects of carvacrol in PTZ-induced seizures in male mice and to investigate the role of N-Methyl-D- Aspartic Acid (NMDA) receptor. Methods. In the present experimental study, 90 mice were randomly divided into 9 groups (n=10). Drugs were injected intraperitoneally 30 minutes before PTZ injection. Then, seizure onset time, serum and brain antioxidant capacity (TAC) and malondialdehyde (MDA) and NMDA receptor gene expression in the hippocampus were examined. Results. Seizure onset time in the group received carvacrol (20 and 40 mg/kg) was significantly longer than the PTZ group (P<0.05). Treatment with carvacrol (20 and 40 mg/kg) significantly increased serum and brain antioxidant capacity and reduced serum and brain MDA compared to the PTZ group (at doses of 5, 10, 20 and 40 mg/kg). The expression of NR2A and NR2B subunits of hippocampal NMDA receptors in carvacrol-received mice was significantly lower than the PTZ group. Conclusion. Carvacrol has anticonvulsant effects, possibly by inhibiting oxidative stress and reducing the expression of subunits of NMDA receptor

Robust graph regularization nonnegative matrix factorization for link prediction in attributed networks

Link prediction is one of the most widely studied problems in the area of complex network analysis, in which machine learning techniques can be applied to deal with it. The biggest drawback of the existing methods, however, is that in most cases they only consider the topological structure of the network, and therefore completely miss out on the great potential that stems from the nodal attributes. Both topological structure and nodes’ attributes are essential in predicting the evolution of attributed networks and can act as complements to each other. To bring out their full potential in solving the link prediction problem, a novel Robust Graph Regularization Nonnegative Matrix Factorization for Attributed Networks (RGNMF-AN) was proposed, which models not only the topology structure of networks but also their node attributes for direct link prediction. This model, in particular, combines two types of information, namely network topology, and nodal attributes information, and calculates high-order proximities between nodes using the Structure-Attribute Random Walk Similarity (SARWS) method. The SARWS score matrix is an indicator structural and attributed matrix that collects more useful attributed information in high-order proximities, whereas graph regularization technology combines the SARWS score matrix with topological and attribute information to collect more valuable attributed information in high-order proximities. Furthermore, the RGNMF-AN employs the ℓ2,1-norm to constrain the loss function and regularization terms, effectively removing random noise and spurious links. According to empirical findings on nine real-world complex network datasets, the use of a combination of attributed and topological information in tandem enhances the prediction performance significantly compared to the baseline and other NMF-based algorithms.</p

A new link prediction in multiplex networks using topologically biased random walks

Link prediction is a technique to forecast future new or missing relationships between nodes based on the current network information. However, the link prediction in monoplex networks seems to have a long background, the attempts to accomplish the same task on multiplex networks are not abundant, and it was often a challenge to apply conventional similarity methods to multiplex networks. The issue of link prediction in multiplex networks is the way of predicting the links in one layer, taking structural information of other layers into account. One of the most important methods of link prediction in a monoplex network is a local random walk (LRW) that captures the network structure using pure random walking to measure nodes similarity of the graph and find unknown connections. The goal of this paper is to propose an extended version of local random walk based on pure random walking for solving link prediction in the multiplex network, referred to as the Multiplex Local Random Walk (MLRW). We explore approaches for leveraging information mined from inter-layer and intra-layer in a multiplex network to define a biased random walk for finding the probability of the appearance of a new link in one target layer. Experimental studies on seven multiplex networks in the real world demonstrate that a multiplex biased local random walk performs better than the state-of-the-art methods of link prediction and corresponding unbiased case and improves prediction accuracy

Impact of Centrality Measures on the Common Neighbors in Link Prediction for Multiplex Networks

Complex networks are representations of real-world systems that can be better modeled as multiplex networks, where the same nodes develop multi-type connections. One of the important concerns about these networks is link prediction, which has many applications in social networks and recommender systems. In this article, similarity-based methods such as common neighbors (CNs) are the mainstream. However, in the CN method, the contribution of each CN in the likelihood of new connections is equally taken into account. In this work, we propose a new link prediction method namely Weighted Common Neighbors (WCN), which is based on CNs and various types of Centrality measures (including degree, k-core, closeness, betweenness, Eigenvector, and PageRank) to predict the formation of new links in multiplex networks. So, in this model, each CN has a different impact on the node connection likelihood. Moreover, we investigate the impact of interlayer information on improving the performance of link prediction in the target layer. Using Area under the ROC Curve and precision as evaluation metrics, we perform a comprehensive experimental evaluation of our proposed method on seven real multiplex networks. The results validate the improved performance of our proposed method compared with existing methods, and we show that the performance of proposed methods is significantly improved while using interlayer information in multiplex networks. </p

A preference random walk algorithm for link prediction through mutual influence nodes in complex networks

Predicting links in complex networks has been one of the essential topics within the realm of data mining and science discovery over the past few years. This problem remains an attempt to identify future, deleted, and redundant links using the existing links in a graph. Local random walk is considered to be one of the most well-known algorithms in the category of quasi-local methods. It traverses the network using the traditional random walk with a limited number of steps, randomly selecting one adjacent node in each step among the nodes which have equal importance. Then this method uses the transition probability between node pairs to calculate the similarity between them. However, in most datasets this method is not able to perform accurately in scoring remarkably similar nodes. In the present article, an efficient method is proposed for improving local random walk by encouraging random walk to move, in every step, towards the node which has a stronger influence. Therefore, the next node is selected according to the influence of the source node. To do so, using mutual information, the concept of the asymmetric mutual influence of nodes is presented. A comparison between the proposed method and other similarity-based methods (local, quasi-local, and global) has been performed, and results have been reported for 11 real-world networks. It had a higher prediction accuracy compared with other link prediction approaches.</p

Spectral clustering on protein-protein interaction networks via constructing affinity matrix using attributed graph embedding

The identification of protein complexes in protein-protein interaction networks is the most fundamental and essential problem for revealing the underlying mechanism of biological processes. However, most existing protein complexes identification methods only consider a network's topology structures, and in doing so, these methods miss the advantage of using nodes' feature information. In protein-protein interaction, both topological structure and node features are essential ingredients for protein complexes. The spectral clustering method utilizes the eigenvalues of the affinity matrix of the data to map to a low-dimensional space. It has attracted much attention in recent years as one of the most efficient algorithms in the subcategory of dimensionality reduction. In this paper, a new version of spectral clustering, named text-associated DeepWalk-Spectral Clustering (TADW-SC), is proposed for attributed networks in which the identified protein complexes have structural cohesiveness and attribute homogeneity. Since the performance of spectral clustering heavily depends on the effectiveness of the affinity matrix, our proposed method will use the text-associated DeepWalk (TADW) to calculate the embedding vectors of proteins. In the following, the affinity matrix will be computed by utilizing the cosine similarity between the two low dimensional vectors, which will be considerable to improve the accuracy of the affinity matrix. Experimental results show that our method performs unexpectedly well in comparison to existing state-of-the-art methods in both real protein network datasets and synthetic networks.</p

Correlation between serum levels of cystatin C and coronary slow flow and body mass index in men

Background: Cystatin C (Cys C) as a cysteine protease inhibitor is produced in a constant level from all nucleated cells. The purpose of this study was to investigate the correlation between serum levels of Cys C and coronary slow flow (CSF) and body mass index (BMI) in men. Methods: This investigation is in the form of a descriptive-analytical study. The statistical population was all non-active male aged 34-73 years with CSF candidate for angiography referring to Seyedoshohada University Hospital, Urmia, Iran, from March 2015 to February 2017. After obtaining an inform consent, 74 male patients (mean age 54.77±9.00 years, height 1.74±0.12 cm, weight 73.13±6.85 kg, and BMI 26.98±3.83 kg/m2) were selected by convenience non-random sampling as the sample size (patients were eligible for diagnostic coronary artery angiography for the first time and referring to Seyedoshohada University Hospital in Urmia). Then all the patients were placed under angiography with one mobile angiography system. Patients were assessed for coronary blood flow with a quantitative method using corrected thrombolysis frame count in myocardial infarction (CTFC). All the patients with TFC larger than two standard deviation pre-published area for a specific vessel were counted as CSF. Demographic characteristics of age, height, weight, and BMI in male patients were measured by wall-meter with an accuracy of one millimeter, digital scale with precision of 100 g, and weight/hieght2 formula, respectively. The traditional risk factors including smoking, diabetes mellitus (DM), high blood pressure (HBP), dyslipidemia, and family history were also assessed using a checklist. Serum levels of Cys C were measured by ELISA machine. Results: The mean demographic and physiological variables of subjects were: age 54.77±9.00 yr, height 1.74±0.12 cm, weight 73.13±6.85 kg, and BMI 26.98±3.83 kg/m2. Also, the results of this study showed that there were no significant correlations between serum levels of Cys C with CSF and BMI in male patients’ candidate for angiography referring to Seyedoshohada University Hospital (P=0.871 and P=0.494, respectively). Conclusion: The results of this study suggest that serum levels of Cys C had no significant correlations with the CSF and BMI in male patients’ candidate for angiography aged 34-73 years

Evaluation of IFN-gamma and HSP70 level in the saliva of Behcet’s disease patients with active oral lesions

Introduction:\ua0Behcet’s disease (BD) is a multi-systemic inflammatory disorder. Evaluating the production of cytokines such as interferon gamma and biomarkers such as heat shock protein-70 (HSP70)is an important way to study the pathogenesis and development of BD. This study aimed to compare the salivary level of interferon gamma and HSP70 between patients infected with BD and healthy individual.Methods:\ua0This case-control study was performed on 35 patients with Behcet’s syndrome and 70 healthy individuals as the control group, who were selected from those referring to the Department of Oral Medicine of Tabriz University of Medical Sciences. The levels of interferon gamma and HSP70 were measured in the whole unstimulated saliva through enzyme-linked immunosorbent assay (ELIZA). In order to compare the quantitative variables between two groups, independent samples t-test or its nonparametric equivalent, Mann-Whitney U test, was used in SPSS software version 16.0. In this study, a\ua0P\ua0value less than 0.05 was considered statistically significant.Results:\ua0There was no significant difference between the study groups in terms of age and gender, as well as salivary interferon gamma and HSP70 levels. Interferon gamma level was 15.16±3.38 pg/mg in the case group and 5.27±1.21 pg/mg in the control group, and salivary HSP70 level was found to be 45.50±17 ng/mL and 19.5±5.2 ng/mL in the case and control groups, respectively.Conclusions:\ua0The results of this study showed that interferon gamma and HSP70 levels in patients with Behcet’s syndrome are high and can be evaluated as an important tool for the treatment and evaluation of disease development in future studies