Islamic Azad University

About: Islamic Azad University is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Population & Adsorption. The organization has 83635 authors who have published 113437 publications receiving 1275049 citations. The organization is also known as: Azad University.

Preparation and in vitro characterization of chitosan nanoparticles containing Mesobuthus eupeus scorpion venom as an antigen delivery system

Abstract: Hydrophilic nanoparticles have been widely investigated in recent years as delivery systems for therapeutic macromolecules such as antigens. In the present study Mesobuthus eupeus venom-loaded chitosan nanoparticles were prepared via ionic gelation of tripolyphosphate (TPP) and chitosan. The optimum encapsulation efficiency (91.1%) and loading capacity (76.3%) were obtained by a chitosan concentration of 2 mg/mL, chitosan-to-TPP mass ratio of 2 and M. eupeus venom concentration of 500 µg/mL. The average nanoparticle size at optimum conditions was determined by Zetasizer (Malvern Instruments, UK). The nanoparticle size was about 370 nm (polydispersity index: 0.429) while the zeta potential was positive. Transmission electron microscope (TEM) imaging showed a spherical, smooth and almost homogenous structure for nanoparticles. Fourier transform infrared (FTIR) spectroscopy confirmed tripolyphosphoric groups of TPP linked with ammonium groups of chitosan in the nanoparticles. The in vitro release of nanoparticles showed an initial burst release of approximately 60% in the first ten hours, followed by a slow and much reduced additional release for about 60 hours. It is suggested that the chitosan nanoparticles fabricated in our study may provide a suitable alternative to traditional adjuvant systems.

Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete

Abstract: Evaluating the in situ concrete compressive strength by means of cores cut from hardened concrete is acknowledged as the most ordinary method, however, it is very difficult to predict the compressive strength of concrete since it is affected by many factors such as different mix designs, methods of mixing, curing conditions, compaction, etc. In this paper, considering the experimental results, three different models of multiple linear regression model (MLR), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) are established, trained, and tested within the Matlab programming environment for predicting the 28 days compressive strength of concrete with 173 different mix designs. Finally, these three models are compared with each other and resulted in the fact that ANN and ANFIS models enables us to reliably evaluate the compressive strength of concrete with different mix designs, however, multiple linear regression model is not feasible enough in this area because of nonlinear relationship between the concrete mix parameters. Finally, the sensitivity analysis (SA) for two different sets of parameters on the concrete compressive strength prediction are carried out.