Novel reduction-responsive hyaluronic acid–chitosan–lipoic acid nanoparticles (HACSLA-NPs) were designed and synthesized for effective treatment of breast cancer by targeting Cluster of Differentiation 44 (CD44)-overexpressing cells and reduction-triggered 17α-Methyltestosterone (MT) release for systemic delivery. The effectiveness of these nanoparticles was investigated by different assays, including release rate, 3-(4,5-Dimethylthiazol-2-Yl)-2,5-Diphenyltetrazolium Bromide (MTT), lactate dehydrogenase (LDH), caspase-3 activity, Rhodamine 123 (RH-123), and Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL). In vitro experiments revealed that Methyltestosterone/Hyaluronic acid–chitosan–lipoic acid nanoparticles (MT/HACSLA-NPs) illustrated a sustained drug release in the absence of glutathione (GSH), while the presence of GSH led to fast MT release. HACSLA-NPs also showed high cellular internalization via CD44 receptors, quick drug release inside the cells, and amended cytotoxicity against positive CD44 BT-20 breast cancer cell line as opposed to negative CD44, Michigan Cancer Foundation-7 (MCF-7) cell line. These findings supported that these novel reduction-responsive NPs can be promising candidates for efficient targeted delivery of therapeutics in cancer therapy.

CO 2 biofixation of the microalgae Chlorella sp. for different CO 2 concentrations and gas flow rates in a bubble column reactor has been investigated in this study. Microalgae were cultivated under different CO 2 concentrations (at 1.75% and 9.45% v/v) and gas flow rates (at 30, 50 and 70 ml/min). The maximum specific growth rate of Chlorella sp. was obtained for the CO 2 concentration of 1.75 % and the gas flow rate of 50 mL/min. The highest biomass productivity rate (at 0.17 g L-1 day-1) was for a sample with 1.75 % CO 2 at a flow rate of 70 ml/min. Moreover, the results have shown that the specific growth rate and CO 2 biofixation have a direct relation with culturing of Chlorella sp. Also, limiting CO 2 supplementation noticeably decreased biomass concentration. Therefore, the results have shown that a high flow rate and low concentration of CO 2 might promote a decrease in CO 2 fixation efficiency by Chlorella sp.

This study aimed to develop pH sensitive polymethacrylic acid-chitosan-polyethylene glycol (PCP) nanoparticles for oral insulin delivery. This was achieved by dispersion polymerization of methacrylic acid (MAA), polyethylene glycol (PEG) and chitosan (CS) in the presence of a cross linking agent, ethylene glycoldimethacrylate (EGDMA), and a polymer initiator, potassium per sulphate. Method development was carried out based on fractional factorial design by varying process parameters such as ratio of MAA to CS, ratio of MAA to EGDMA and the initial amount of insulin used to prepare PCP nanoparticles. PCP nanoparticles were characterized with different techniques including FTIR, DLS, and scanning electron microscopy (SEM). Insulin was incorporated into the nanoparticles by a diffusion filling method. It was found that the PCP nanoparticles exhibited good protein encapsulation efficiency (up to 99.9%). The findings revealed that the nanoparticles were spherical with smooth surfaces. The particle size average was determined to be 172 nm by DLS and 86 nm by SEM. The in vitro release profiles of PCP nanoparticles were investigated both in acidic (simulated gastric fluids, pH: 1.2) and neutral buffered solutions (simulated intestinal fluids, pH: 7.4). In order to have the best performance of nanoparticles, the process parameters were optimized using a support vector regression (SVR) method in combination with genetic algorithms (GA). The results revealed that the optimum settings were as follows: MAA/CS mole ratio (%): 297.35, CS/EGDMA mole ratio (%): 51.4, and the initial insulin amount (mg): 50.3. The findingsshowed that nanoparticles exhibited a pH responsive release profile where the extent of drug release in simulated intestinal medium was almost two fold more than the simulated gastric media. Global sensitivity analysis was also used to identify the impact of different variables on the PCP nanoparticle characteristics. This study introduces a new approach to rational design of nanoparticles according to the properties of interest.

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