Optimizing Fenton-like process, homogeneous at neutral pH for ciprofloxacin degradation: Comparing RSM-CCD and ANN-GA.

Antibiotics are considered among the most non-biodegradable environmental contaminants due to their genetic resistance. Considering the importance of antibiotics removal, this study was aimed at multi-objective modeling and optimization of the Fenton-like process, homogeneous at initial circumneutral pH. Two main issues, including maximizing Ciprofloxacin (CIP) removal and minimizing sludge to iron ratio (SIR), were modeled by comparing central composite design (CCD) based on Response Surface Methodology (RSM) and hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA). Results of simultaneous optimization using ethylene diamine tetraacetic acid (EDTA) revealed that at pH â‰… 7, optimal conditions for initial CIP concentration, Fe2+ concentration, [H2O2]/[Fe2+] molar ratio, initial EDTA concentration, and reaction time were 14.9 mg/L, 9.2 mM, 3.2, 0.6 mM, and 25 min, respectively. Under these optimal conditions, CIP removal and SIR were predicted at 85.2% and 2.24 (gr/M). In the next step, multilayer perceptron (MLP) and radial basis function (RBF) artificial neural networks (ANN) were developed to model CIP and SIR. It was concluded that ANN, especially multilayer perceptron (MLP-ANN) has a decent performance in predicting response values. Additionally, multi-objective optimization of the process was performed using Genetic Algorithm (GA) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to maximize CIP removal efficiencies while minimizing SIR. NSGA-II optimization algorithm showed a reliable performance in the interaction between conflicting goals and yielded a better result than the GA algorithm. Finally, TOPSIS method with equal weights of the criteria was applied to choose the best alternative on the Pareto optimal solutions of the NSGA-II. Comparing the optimal values obtained by the multi-objective response surface optimization models (RSM-CCD) with the NSGA-II algorithm showed that the optimal variables in both models were close and, according to the absolute relative error criterion, possessed almost the same performance in the prediction of variables.

Bioactive Agent-Loaded Electrospun Nanofiber Membranes for Accelerating Healing Process: A Review.

Despite the advances that have been achieved in developing wound dressings to date, wound healing still remains a challenge in the healthcare system. None of the wound dressings currently used clinically can mimic all the properties of normal and healthy skin. Electrospinning has gained remarkable attention in wound healing applications because of its excellent ability to form nanostructures similar to natural extracellular matrix (ECM). Electrospun dressing accelerates the wound healing process by transferring drugs or active agents to the wound site sooner. This review provides a concise overview of the recent developments in bioactive electrospun dressings, which are effective in treating acute and chronic wounds and can successfully heal the wound. We also discuss bioactive agents used to incorporate electrospun wound dressings to improve their therapeutic potential in wound healing. In addition, here we present commercial dressings loaded with bioactive agents with a comparison between their features and capabilities. Furthermore, we discuss challenges and promises and offer suggestions for future research on bioactive agent-loaded nanofiber membranes to guide future researchers in designing more effective dressing for wound healing and skin regeneration.

Introduction of magnetic and supermagnetic nanoparticles in new approach of targeting drug delivery and cancer therapy application.

In this article, the recent applications of different types of magnetic nanoparticles such as α-Fe2O3 (hematite), γ-Fe2O3 (maghemite), Fe3O4 (magnetite), hexagonal (MFe12O19), garnet (M3Fe5O12) and spinel (MFe2O4), where M represents one or more bivalent transition metals (Mn, Fe, Co, Ni, Ba, Sr, Cu, and Zn), and different materials for coating the surface of magnetic nanoparticles like poly lactic acid (PLA), doxorubicin hydrophobic (DOX-HCL), paclitaxel (PTX), EPPT-FITC, oleic acid, tannin, 3-Aminopropyltriethoxysilane (APTES), multi-wall carbon nanotubes (CNTs), polyethylenimine (PEI) and polyarabic acid in drug delivery, biomedicine and treatment of cancer, specially chemotherapy, are reviewed. MNPs possess large surface area to volume ratios because of their nano-size, low surface charge at physiological pH and they aggregate easily in solution due to their essential magnetic nature. These materials are widely used in biology and medicine in many cases. One targeted delivery technique that has gained prominence in recent years is the use of magnetic nanoparticles. In these systems, therapeutic compounds are attached to biocompatible magnetic nanoparticles and magnetic fields generated outside the body are focused on specific targets in vivo. The fields capture the particle complex, resulting in enhanced delivery to the target site. Also, the application of brand new supermagnetic nanoparticles, like Ba,SrFe12O19, is considered and studied in this paper.

Data on heavy metal concentration in common carp fish consumed in Shiraz, Iran.

Food contamination by heavy metals can lead to the accumulation of these elements in the body of consumers and the contraction of diseases. Accordingly, heavy metal concentration in common carp fishes consumed in Shiraz, Iran was determined in the present study. The mean concentrations of Pb, Cd, Zn, and Cu were 0.23, 0.07, 0.47, and 0.59 mg/kg (dry weight), respectively. The average concentration of heavy metals in the muscle of common carps consumed in Shiraz was less than the permissible standard of the WHO and FAO. The estimated weekly intake (EWI) of the studied metals was below the provisional tolerable weekly intake (PTWI). The maximum and minimum relative risk (RR) equaled 48.93 and 0.55% of the total risk for Cd and Zn, respectively.

Degradation of ciprofloxacin antibiotic by Homogeneous Fenton oxidation: Hybrid AHP-PROMETHEE method, optimization, biodegradability improvement and identification of oxidized by-products.

The main purpose of this experimental study was to optimize Homogeneous Fenton oxidation (HFO) and identification of oxidized by-products from degradation of Ciprofloxacin (CIP) using hybrid AHP-PROMETHEE, Response Surface Methodology (RSM) and High Performance Liquid Chromatography coupled with Mass Spectrometry (HPLC-MS). At the first step, an assessment was made for performances of two catalysts (FeSO4·7H2O and FeCl2·4H2O) based on hybrid AHP-PROMETHEE decision making method. Then, RSM was utilized to examine and optimize the influence of different variables including initial CIP concentration, Fe2+ concentration, [H2O2]/[ Fe2+] mole ratio and initial pH as independent variables on CIP removal, COD removal, and sludge to iron (SIR) as the response functions in a reaction time of 25 min. Weights of the mentioned responses as well as cost criteria were determined by AHP model based on pairwise comparison and then used as inputs to PROMETHEE method to develop hybrid AHP-PROMETHEE. Based on net flow results of this hybrid model, FeCl2·4H2O was more efficient because of its less environmental stability as well as lower SIR production. Then, optimization of experiments using Central Composite Design (CCD) under RSM was performed with the FeCl2·4H2O catalyst. Biodegradability of wastewater was determined in terms of BOD5/COD ratio, showing that HFO process is able to improve wastewater biodegradability from zero to 0.42. Finally, the main intermediaries of degradation and degradation pathways of CIP were investigated with (HPLC-MS). Major degradation pathways from hydroxylation of both piperazine and quinolonic rings, oxidation and cleavage of the piperazine ring, and defluorination (OH/F substitution) were suggested.

Quality assessment and artificial neural networks modeling for characterization of chemical and physical parameters of potable water.

Today, due to the increase in the population, the growth of industry and the variety of chemical compounds, the quality of drinking water has decreased. Five important river water quality properties such as: dissolved oxygen (DO), total dissolved solids (TDS), total hardness (TH), alkalinity (ALK) and turbidity (TU) were estimated by parameters such as: electric conductivity (EC), temperature (T), and pH that could be measured easily with almost no costs. Simulate water quality parameters were examined with two methods of modeling include mathematical and Artificial Neural Networks (ANN). Mathematical methods are based on polynomial fitting with least square method and ANN modeling algorithms are feed-forward networks. All conditions/circumstances covered by neural network modeling were tested for all parameters in this study, except for Alkalinity. All optimum ANN models developed to simulate water quality parameters had precision value as R-value close to 0.99. The ANN model extended to simulate alkalinity with R-value equals to 0.82. Moreover, Surface fitting techniques were used to refine data sets. Presented models and equations are reliable/useable tools for studying water quality parameters at similar rivers, as a proper replacement for traditional water quality measuring equipment's.