Identifying brain regions contributing to Alzheimer's disease using self regulating particle swarm optimization

In this article, we developed an approach for detecting brain regions that contribute to Alzheimer's disease (AD) using support vector machine (SVM) classifiers and the recently developed self regulating particle swarm optimization (SRPSO) algorithm. SRPSO employs strategies inspired by the principles of learning in humans to achieve faster and better optimization results. The classifiers for distinguishing subjects into AD patients and cognitively normal (CN) individuals were built using grey matter (GM) and white matter (WM) volumetric features extracted from structural magnetic resonance (MR) images. It could be observed from results that the classifier built using both GM and WM features provided accuracy of 89.26% which is better than the performance of classifiers built using either GM or WM features only. Moreover, consideration of clinical features in addition to volumetric features improves the accuracy further to 94.63% which is better than the performance reported by recent works in literature. In order to identify the brain regions that are important for AD vs CN classification problem, we used SRPSO to extract GM and WM features that yield better classification performance. Using 50 features identified by SRPSO, an accuracy of 89.39% was obtained which is close to the accuracy based on all features. The features identified by SRPSO were mapped back to the brain to identify brain regions that exhibit degeneration in AD. In addition to identifying areas known to be involved in AD like cerebellum, hippocampus, this helped in finding newer areas that might contribute towards AD.     

Design and optimization of alginate−chitosan−pluronic nanoparticles as a novel meloxicam drug delivery system

The inflammation and pain associated with osteoarthritis are treated with nonsteroidal anti‐inflammatory drugs (NSAIDs). This treatment is accompanied by several side effects; therefore local intra articular (IA) NSAID injection can be more efficient and safe than systemic administration or topical use. In this study, alginate?chitosan?pluronic nanoparticles were considered as a new vehicle for IA meloxicam delivery. These novel nanoparticles were prepared using an ionotropic gelation method and were optimized for variables such as alginate to chitosan mass ratio, pluronic concentration, and meloxicam concentration using a 3‐factor in 3‐level Box‐Behnken design. To optimize the formulation, the dependent variables considered were particle size, zeta potential, entrapment efficiency, and mean dissolution time (MDT). The nanoparticles morphology was characterized by FESEM and AFM. The potential interactions of the drug‐polymers were investigated by ATR‐FTIR and DSC, and the delivery profile of meloxicam from the nanoparticles was obtained. The average particle size of the optimized nanoparticles was 283 nm, the zeta potential was ?16.9 mV, the meloxicam entrapment efficiency was 55%, and the MDT was 8.9 hours. The cumulative released meloxicam amount from the composite nanoparticles was 85% at pH 7.4 within 96 h. The release profile showed an initial burst release followed by a sustained release phase. The release mechanism was non‐Fickian diffusion. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 42241.     

Novel corona discharge treatment of cotton fabric with Cu and ZnO nanoparticles

Abstract

The aim of this study was to investigate coating of copper and zinc oxide nanoparticles on cotton fabric by using corona discharge in two ways of pre-treatment and post-treatment. In pre-treatment method, cotton fabrics were modified by corona discharge before coating separately with nanoparticles. In post-treatment, cotton fabrics were coated separately with ZnO and Cu nanoparticles before treating by corona discharge. Self-cleaning properties of treated fabrics were determined by staining methylene blue dye. The antibacterial tests, Scanning electron microscopy and FTIR/ATR analysis were carried out to observe antibacterial performance, surface morphology and analyze the surface chemical structure, respectively. Atomic absorption spectroscopy and water droplet adsorption were used for the determination of metal ion content, and water adsorption. Results showed that by pre-treatment method of corona discharge, absorption of copper nanoparticles was increased, and self-cleaning effect and antibacterial performance of copper nanoparticles were higher than post-treatment. ZnO nanoparticles had highest self-cleaning and antibacterial effect by pre-treatment method. By post-treatment method, the photocatalyst activity of ZnO nanoparticles was decreased.