Optimization of adaptive neuro-fuzzy inference system (ANFIS) parameters via Box-Behnken experimental design approach: The prediction of chromium adsorption.

Prediction of adsorption via Adaptive Neuro-Fuzzy Inference System (ANFIS) can save the cost and time in practical applications. Chromium (VI) adsorption data obtained at different temperature, activated carbon dosage and pH values were evaluated by using MATLAB ANFIS. In order to achieve prediction of adsorption via ANFIS with acceptable error values, optimum membership function (MF) and optimum number of MF were determined by using Box-Behnken experimental design (BBD) method. In order to determine the optimum number of MF for each input, all combinations given in BBD matrix were examined via ANFIS, then, regression models for each MFs were developed between the root mean square error (RMSE) and MF numbers of each input. The most used five membership functions (triangular, trapezoidal, generalized bell shaped, Gaussian, Gaussian 2) were investigated. According to the analysis of variance (ANOVA), regression models developed for the test data with triangular and trapezoidal membership functions were significant in the 95 % confidence level. Predictions were employed via ANFIS by using optimum MF numbers of each inputs (6, 6, 3 for triangular MF and 8, 8, 2 for trapezoidal MF). Consequently, the best Cr(VI) adsorption percentage prediction (RMSE = 1.9084 and R2 = 0.992) was obtained by using triangular membership function with optimum MF numbers. Response surface plots, which gives the relationship between MF numbers and RMSE values for triangular MF were also evaluated. In this study, it was demonstrated that MF type and numbers, which are crucial for good prediction via ANFIS grid partition method, can be determined optimally by applying experimental design methodology.

Comparative evaluation of hesperetin loaded nanoparticles for anticancer activity against C6 glioma cancer cells.

The aim of this study was to evaluate anti-cancer properties of hesperetin (Hsp) and hesperetin-loaded poly(lactic-co-glycolic acid) nanoparticles (HspNPs) for glioblastoma treatment. Nanoparticles prepared by single emulsion method had a size of less than 300 nm with 70.7 ± 3.9% reaction yield and 26.4 ± 1.1% Hsp loading capacity. Treatment of C6 glioma cells with HspNPs for 24 and 48 h resulted in dose- and time-dependent decrease in cell viability, with approximate IC50 of 28 and 21 µg/mL, respectively (p = .036 for 24 h, p = .025 for 48 h). The percentage of PCNA positive cells decreased to 20% and 10%, respectively, for Hsp- and HspNP-treated cells at concentration of 100 µg/mL. Treatment with increasing concentrations of HspNPs (25, 50, 75 and 100 µg/mL) resulted in 9.1-, 7-, 12.5- and 12.7-fold in increase in apoptotic cell number. Optimum doses of Hsp and HspNPs were found to increase oxidative damage in C6 glioma cells. MDA levels, an indicator of lipid peroxidation, were found to be significantly elevated at 75 and 100 µg/mL exposure concentration of HspNPs with (p = .002) and (p = .018), respectively for 48-h treatment. The results obtained with this study showed biocompatible polymeric nanoparticle systems has great advantages to enhance anti-cancer activity and poor solubility of therapeutic agents. Overall our findings suggest that Hsp-loaded PLGA nanoparticle systems showed significant anti-cancer activity and HspNPs could be used as promising novel anti-cancer agent.

Synthesis of hesperetin-loaded PLGA nanoparticles by two different experimental design methods and biological evaluation of optimized nanoparticles.

Hesperetin was effectively encapsulated into poly (d,l-lactic-co-glycolic acid) nanoparticles by using experimental design methods. A seven-factor Plackett-Burman design was used in order to determine the major process parameters. A significant linear equation, which shows the effect of each process parameter on encapsulation efficiency was developed, and then the most effective factors were determined. Further investigation and optimization was carried out by applying the three-factor three-level Box-Behnken design. Significant second-order mathematical models were developed by regression analysis of the experimental data for both responses: encapsulation efficiency and nanoparticle size. The two step experimental design allowed the synthesis of the desired nanoparticle formulations with maximum encapsulation efficiency (80.5 ± 4.9%) and minimum particle size (260.2 ± 16.5 nm) at optimum process conditions: 0.5% polyvinyl alcohol (PVA) concentration, 5.13 water:organic phase ratio, and 3.59 ml min-1 flow rate of the emulsified solution into 0.1% PVA. Furthermore, the biological activity of these optimized nanoparticles were determined with antimicrobial activity and cytotoxicity studies; results were then compared to the free hesperetin. The cytotoxicity result revealed that hesperetin and hesperetin-loaded nanoparticles were biocompatible with normal cell line L929 fibroblast cells up to 184.83 and 190.88 µg ml-1 for 24 h, and up to 133.24 and 134.80 µg ml-1 for 48 h, respectively. In the antimicrobial study, the optimized nanoparticle showed inhibition activity (minimal inhibitory concentration (MIC) values were 125 µg ml-1 for Escherichia coli, and 200 µg ml-1 for Staphylococcus aureus), while the free hesperetin did not demonstrate activity in both strains (MIC value >200 µg ml-1). These in vitro results may provide useful information for the investigation of hesperetin-loaded nanoparticles in diagnostic and therapeutic applications.

Preparation of Fe oxide nanoparticles for environmental applications: arsenic removal.

The objective of this study is to examine the adsorption-desorption behavior of a magnetically active hybrid sorbent (MAHS) material, prepared by dispersing colloid-like hydrated iron oxide particles in the outer periphery of a macroporous ion-exchange resin (Amberlite XAD-2). The experimental results show that the new sorbent material can simultaneously remove arsenic (V) and a chlorinated organic compound (2,6-dichlorophenol [2,6-DCP]) from aqueous solutions at around neutral pH. The recovery of arsenic and 2,6-DCP from MAHS was conducted using a regenerant containing 50% (v/v) CH3OH + 3% (w/v) NaOH. In less than 10 bed volumes of regenerant, more than 90% of As(V) and 2,6-DCP were recovered.