Kharazmi University

About: Kharazmi University is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Membrane & Supply chain. The organization has 3395 authors who have published 5321 publications receiving 45645 citations. The organization is also known as: Tarbiat Moallem University of Tehran & Teacher Training University.

A nanocomposite prepared from reduced graphene oxide, gold nanoparticles and poly(2-amino-5-mercapto-1,3,4-thiadiazole) for use in an electrochemical sensor for doxorubicin

Abstract: A nanocomposite was prepared with reduced graphene oxide, gold nanoparticles and an electropolymerized film made from 2-amino-5-mercapto-1,3,4-thiadiazole. An electrochemical sensor for doxorubicin (DOX) was constructed by modifying a glassy carbon electrode (GCE) with the nanocomposite. The modified GCE was studied by electrochemical techniques which showed it to enable highly sensitive sensing of DOX. Response (typically measured at a typical working potential of −0.56 V vs. Ag/AgCl) is linear in the 30 pM to 30 nM and 30 nM to 30 μM DOX concentration ranges, with a limit of detection (LOD) of 9 pM (at an S/N ratio of 3). The method was applied to the determination of DOX in serum and gave recoveries that ranged between 92 and 108%.

Optimizing a multi-item economic order quantity problem with imperfect items, inspection errors, and backorders

Abstract: This paper proposes a multi-item economic order quantity model with imperfect items in supply deliveries. The inspection process to classify the items is not perfect and involves two types of error: Type-I and Type-II. To cope with the uncertainty involved in real-world applications and to bring the problem closer to reality, operational constraints are assumed stochastic. The aim is to determine the optimal order and back order sizes of the items in order to achieve maximum total profit. As the proposed mathematical model is a constrained nonlinear programming, three different solution methods including an exact method named the interior-point and two novel meta-heuristics named grey wolf optimizer (GWO) and moth-flame optimization (MFO) algorithms are utilized to solve the problem. In order to demonstrate the most efficient solution method, the performance of the three solution methods is evaluated when they solve some test problems of different sizes. Various comparison measures including percentage relative error, relative percentage deviation, and computation time are used to compare the solution methods. Based on the results, MFO performs better in small and medium instances in terms of percentage relative error; meanwhile, GWO shows a better performance in terms of relative percentage deviation in large-size test problems. In the end, sensitivity analyses are carried out to investigate how any parameter change affects the objective function value of the mathematical model in order to determine the most critical parameter.

Comparison of different protocols for neural differentiation of human induced pluripotent stem cells.

Abstract: Although embryonic stem cells (ESCs) have enormous potentials due to their pluripotency, their therapeutic use is limited by ethical, biological and safety issues. Compared to ESCs, induced pluripotent stem cells (iPSCs) can be obtained from mouse or human fibroblasts by reprogramming. Numerous studies have established many protocols for differentiation of human iPSCs (hiPSCs) into neural lineages. However, the low differentiation efficiency of such protocols motivates researchers to design new protocols for high yield differentiation. Herein, we compared neural differentiation potential of three induction media for conversion of hiPSCs into neural lineages. In this study, hiPSCs-derived embryoid bodies were plated on laminin coated dishes and were treated with three induction media including (1) bFGF, EGF (2) RA and (3) forskolin, IBMX. Immunofluorescence staining and quantitative real-time PCR (qPCR) analysis were used to detect the expression of neural genes and proteins. qPCR analysis showed that the expression of neural genes in differentiated hiPSCs in forskolin, IBMX supplemented media was significantly higher than undifferentiated cells and those in induction media containing bFGF, EGF or RA. In conclusion, our results indicated a successful establishment protocol with high efficiency for differentiation of hiPSCs into neural lineages.

The synergistic cytotoxic effect of cisplatin and honey bee venom on human ovarian cancer cell line A2780cp.

Abstract: Ovarian cancer is considered to be one of the most important causes of death among women. Cisplatin is one of the oldest chemotherapeutical compounds used for treating ovarian cancer. Previous studies have shown the inhibitory effects of bee venom on certain types of cancer. The aim of the present study was to evaluate the cytotoxic effect of bee venom alone and its synergistic cytological effects in combination with cisplatin on ovarian cancerous cisplatin resistant A2780cp cells. To investigate the cytotoxic effect of bee venom on A2780cp cells and its synergetic effect with cisplatin, MTT assay, morphological examination, DNA fragmentation assay, flowcytometric and immunocytochemical analysis were performed. MTT assay revealed that 8µg/ml bee venom, 25mg/ml cisplatin and 4µg/ml bee venom/10mg/ml cisplatin cause an approximately 50% A2780cp cell death after 24hr. Morphological and biochemical analysis indicated an apoptotic type of cell death induced by bee venom and cisplatin, separately and in combination. Immunocytochemistry demonstrated a reduction in the levels of the Bcl2 protein. Overall, our findings suggest that components of bee venom may exert an anti-tumor effect on human ovarian cancer and that has the potential for enhancing the cytotoxic effect of the antitumor agent cisplatin.