Colon cancer is one of the most common spread cancers in the world, which leads to total death of 10%. Prediction of onset of cancer, and the cause of its development in these patients can be of an enormous help and relief to those affected, as they can get back their “normal” life. Data mining and machine learning are important intelligent tools for classification, prediction and hidden relation extraction between patient information. We collected data from Shahid Faghihi Hospital in Shiraz. Features collected are as follows: Gender, age, duration of cancer before surgery, number of times the patients used bathroom, taking anti-inflammatory drug prednisolone, duration of drug use and dosage, kind of surgery and number of times consulted and retreatment of surgery, incontinence, etc. After pre-processing and data cleaning stages, effective features were extracted, and also occurrence of cancer predicts by using different classification algorithms. Then association rule mining algorithms like Apriori were used for obtaining any internal hidden relation between entries. Approaching them with different algorithms and assessing them with support vector machine was with highest prediction accuracy (84%). Due to unbalanced dataset, we chose cost sensitive support vector machine. In another aspect, after applying Apriori algorithm, the conditions of non-inflammation were extracted based on dataset features. Some significant outcomes are in what follows. If surgery treatment or diagnosed was less than 5 years, the possibility of developing colon cancer is lower. Also, as the duration of disease increases, the possibility of reoperation increases, as confirmed by the interiors. Since this issue with these features was raised for the first time in this paper at the suggestion of internists, early detection of cancer and also the extraction of effective laws can be of help to the medical community. In future, to get higher accuracy, the improvement of the dataset in terms of number of samples and colonoscopy image features is considered.

Background: Streptococcus mutans is one of the most important microorganisms in tooth decay. Sortase A (SrtA) of S. mutans is responsible for the attachment of bacteria to the host cell and biofilm formation. Therefore, it seems necessary to investigate the inhibitors of this enzyme to prevent dental caries. Chalcones are always of interest in the medical community due to their wide range of biological activities. Many studies have reported that chalcone can help prevent caries. The present study was conducted to identify potential SrtA inhibitors with the chalcone skeleton. Methods: The chalcone derivatives were obtained from the ZINC15, LEA3D, and PubChem databases, and then the selected compounds were optimized by HyperChem software. The affinity of these compounds to SrtA and total binding free energy (ΔGbind) were estimated by the AutoDock 4.0 program. Finally, drug-likeness screening and absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of the best ligands were obtained using online servers. Results: Compared to chalcone, four of the studied ligands, including compounds 2, 7, 8, and 9 demonstrated high affinity for binding to S. mutans SrtA, with suitable drug-likeness and ADMET properties. Ligand 9 interacted with the key residues in the active site by the most negative ΔGbind (-4.64 kcal/mol). The best conformation of this ligand had the most overlap with the chalcone. Conclusion: By complementary both in vitro and in vivo studies on the inhibitory effects of compounds 2, 7, 8, and 9, the present study can be useful in controlling tooth decay and dental diseases.

Application of a Clinical Information System (CIS) like Electronic Patient Record (EPR), PACS system and CPOE has turned into one of the most important criteria of priorities of health care systems. The aims of the clinical information system include improving the physicians' efficiency level, integrating the caring process, and expanding the fuzzy quality of the services offered to patients. Achievement of these benefits in reality is not an easy task, and there are lots of plans in this field which are doomed to failure. About 50% of the implementation plans of clinical information systems in health care organizations have failed, and this trend is significantly affecting industrial countries. Proper implementation of hospital information systems lies in identifying and assessing the relationships among the most important risk factors of fuzzy. The present study aimed to provide an applicable model for identifying, ranking and evaluating the risk factors associated with projects of clinical information technology in hospitals of Shiraz University of Medical Sciences. This is an applied study which evaluates the risk factors associated with implementation of clinical information technology projects in hospitals of Shiraz Medical Sciences University. The participants consisted of professionals and senior experts of clinical information technology. Fuzzy logic was used in this study. We also applied ANP-DEMATEL combined model with fuzzy procedure to provide the analytic model of the study. According to the study findings, lack of top-executive supports, and unstable organizational environment were the two most important risk factors, while the main organizational factors and technology were also highly important. In addition, the factors associated with technology had the highest influence on the other studied risk factors. Hospital authorities can benefit from this proposed model to reduce the risk of implementing the projects of clinical information technology and improve the success coefficient of the risk of such projects.

The flexibility of cloud computing to provide a dynamic and adaptable infrastructure in the context of information technology and service quality has made it one of the most challenging issues in the computer industry. Task scheduling is a major challenge in cloud computing. Scheduling tasks so that they may be processed by the most effective cloud network resources has been identified as a critical challenge for maximizing cloud computing’s performance. Due to the complexity of the issue and the size of the search space, random search techniques are often used to find a solution. Several algorithms have been offered as possible solutions to this issue. In this study, we employ a combination of the genetic algorithm (GA) and the gravitational emulation local search (GELS) algorithm to overcome the task scheduling issue in cloud computing. GA and the particle swarm optimization (PSO) algorithms are compared to the suggested algorithm to demonstrate its efficacy. The suggested algorithm outperforms the GA and PSO, as shown by the experiments.

Oscillations are an intrinsic phenomenon in interconnected power systems, leading to steady-state stability, safety decline, transmission power limitation, and electric power systems’ ineffective exploitation by developing power systems, particularly by connecting these systems to low-load lines. In addition, they affect the economic performance of the systems. In this study, PSS2B power system stabilizers and TCSC compensators are used to improve the stability margin of power systems. In order to coordinate TCSC compensators, the MOPSO multiobjective algorithm with integral of the time-weighted absolute error (ITAE) and figure of demerit (FD) objective functions was used. The MOPSO algorithm optimization results are compared with nondominated sorting genetic algorithm (NSGAII) and multiobjective differential evolution (MODE) algorithms. The optimization results indicated a better performance of the proposed MOPSO algorithm than NSGAII and MODE. The simulations were iterated in two scenarios by creating different loading conditions in generators. The results indicated that the proposed control system, where the coordination between PSS2B power system stabilizers and TCSC compensators using the MOPSO algorithm, is better than power systems in which PSS2B Stabilizers or TCSC compensators are utilized solely. All criteria, e.g., ITAE, FD, maximum deviation range, and the required time for power oscillation damping in hybrid control systems, have been obtained. This means more stability and accurate and proper performance.

PurposeCell signaling pathways play central roles in cellular stemness state, and aberrant activation of these cascades is attributed to the severity of esophageal squamous cell carcinoma (ESCC). In this study, we aimed to determine the potential impact of enhancer of zeste homolog 2 (EZH2) gene on different cell signaling pathways including bone morphogenesis protein (BMP), Hedgehog, and Hippo in ESCC, and to illuminate EZH2-mediated gene regulatory networks in this aggressive malignancy. Materials and methodsEZH2 silencing was performed in two ESCC lines, KYSE-30 and YM-1, followed by gene expression analysis of BMP, Hedgehog, and Hippo signaling using RT-qPCR. EZH2 enforced expression was induced in both cell lines and gene expression of the pathways was evaluated in parallel. The contribution of EZH2 in epithelial-mesenchymal transition (EMT) and cell migration were also evaluated. ResultsEZH2 downregulation decreased expression of the vital components of the Hedgehog and Hippo signaling, while EZH2 upregulation significantly increased its levels in both ESCC cell lines. The expression of BMP target genes was either reduced in EZH2-expressing cells or increased in EZH2-silencing cells. Enforced expression of EZH2 stimulated downregulation of epithelial markers and upregulation of mesenchymal markers in KYSE-30 and YM-1 ​cells. Significant downregulation of mesenchymal markers was detected following the silencing of EZH2 in the cells. Knocking down EZH2 decreased migration, while enforced expression of EZH2 increased migration in both ESCC lines. ConclusionsThese results may support the promoting role of EZH2 in ESCC tumorigenesis through the recruitment of important cell signaling pathways.

Identification of nodes that spread influence is an important aspect of social network analysis. These nodes are used for maximizing influence. Influence maximization (INMAXI) is basically NP-Hard. This issue, with large-scale data, faces many challenges such as accuracy and efficiency. This paper offers a new approach in this area, named RSP (Reducing search space in INMAXI). The RSP algorithm uses centralities and shells of social networks for selecting super-spreaders. The nodes in the shortest path are of great importance in the RSP algorithm. Unlike other algorithms, this algorithm does not ignore low-degree nodes. Experiments indicate that the RSP algorithm works better than RNR, MCGN, LMP, and LIR on influence spread and maintains the quality of the results in every way.

Objective: Genetic toxicity examines the health of those exposed to mutagenic agents and the mutagenic impacts of radiation and chemicals. The process of causing genetic alterations and DNA damage is known as mutagenesis. Therefore, it is vital to carry out research in this area at many levels and come up with suitable ways to stop or lessen the development of genetic toxicity. The sodium version of benzoic acid, sodium benzoate, is a common ingredient in both food and cosmetics. This study aims to explore the genotoxicity produced by sodium benzoate in the liver cancer cell line (HepG2) using the Comet assay because of the substance's widespread use. Method: The comet assay or single cell gel electrophoresis (SCGE) is a quick and accurate method for identifying and assessing DNA damage in single cells. One-way ANOVA and Tukey's multiple comparison posthoc test were used to examine the results, which were shown as the SD Mean of the report and the data connected to the Comet indices. The difference's significance threshold was set at p<0.0001. Findings: DNA damage was generated in the liver cancer cell line in the current investigation by sodium benzoate's genotoxicity at concentrations of 50, 100, and 150 micromolar (HepG2).