Iron oxide nanoparticles supported on mesoporous silica‐type materials have been successfully utilized in the aqueous selective oxidation of alkenes under mild conditions using hydrogen peroxide as green oxidant. The supported catalyst could be easily recovered after completion of the reaction and reused several times without any loss in activity (no metal leaching observed during the reaction), constituting a facile and straightforward example of aqueous oxidation chemistry promoted by iron‐based heterogeneous systems.

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Background:Epilepsy is a chronic neurological disorder characterized by seizure recurrence in patients. Electroencephalogram (EEG) has a diagnostic and prognostic role in the management of patients. Studies have shown a significant relation between seizure recurrence and abnormal EEG in newly diagnosed epileptic patients, and people with first episode of unprovoked seizure. The aim of this study is to evaluate seizure frequency in chronic epileptic patients on drug therapy based on normal or abnormal EEG.Materials and Methods:This prospective cohort study examined seizure recurrence in 59 epileptic patients (50.8% generalized, 49.2% focal) with normal and abnormal EEG. Data were recorded in patient medical file, and patients were followed by telephone call or visiting by neurologist.Results:In this study, 59 patients with a mean age of 29.58 ± 10.37 years were assessed that 42.4% of them were males and 57.6% were females. Seizure frequency in patient with specific abnormal EEG was significantly more than other patients (specific abnormal: 78.9%, nonspecific abnormal: 45.5%, and normal: 31%, P = 0.005). Seizure recurrence in patients on polytherapy was significantly higher than others (polytherapy: 76.9% and monotherapy: 27.3%, P < 0.001). In patient with abnormal imaging seizure, frequency was more than other patients which was nearly significant (P = 0.054).Conclusion:Abnormal EEG and number of anticonvulsant drugs have a role in seizure recurrence in epileptic patients.

IntroductionMultiple sclerosis (MS), a non-contagious and chronic disease of the central nervous system, is an unpredictable and indirectly inherited disease affecting different people in different ways. Using Omics platforms genomics, transcriptomics, proteomics, epigenomics, interactomics, and metabolomics database, it is now possible to construct sound systems biology models to extract full knowledge of the MS and recognize the pathway to uncover the personalized therapeutic tools.MethodsIn this study, we used several Bayesian Networks in order to find the transcriptional gene regulation networks that drive MS disease. We used a set of BN algorithms using the R add-on package bnlearn. The BN results underwent further downstream analysis and were validated using a wide range of Cytoscape algorithms, web based computational tools and qPCR amplification of blood samples from 56 MS patients and 44 healthy controls. The results were semantically integrated to improve understanding of the complex molecular architecture underlying MS, distinguishing distinct metabolic pathways and providing a valuable foundation for the discovery of involved genes and possibly new treatments.ResultsResults show that the LASP1, TUBA1C, and S100A6 genes were most likely playing a biological role in MS development. Results from qPCR showed a significant increase (P &lt; 0.05) in LASP1 and S100A6 gene expression levels in MS patients compared to that in controls. However, a significant down regulation of TUBA1C gene was observed in the same comparison.ConclusionThis study provides potential diagnostic and therapeutic biomarkers for enhanced understanding of gene regulation underlying MS.