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.

Epidermal neural crest stem cell-derived glia enhance neurotrophic elements in an ex vivo model of spinal cord injury.

Abstract: Growing evidence that cell-based therapies can improve recovery outcome in spinal cord injury (SCI) models substantiates their application for treatment of human with SCI. To address the effectiveness of these stem cells, potential candidates should be evaluated in proper SCI platform that allows direct real-time monitoring. In this study, the role of epidermal neural crest stem cells (EPI-NCSCs) was elucidated in an ex vivo model of SCI, and valproic acid (VPA) was administered to ameliorate the inhospitable context of injury for grafted EPI-NCSCs. Here the contusion was induced in organotypic spinal cord slice culture at day seven in vitro using a weight drop device and one hour post injury the GFP- expressing EPI-NCSCs were grafted followed by VPA administration. The evaluation of treated slices seven days after injury revealed that grafted stem cells survived on the injured slices and expressed GFAP, whereas they did not express any detectable levels of the neural progenitor marker doublecortin (DCX), which was expressed prior to transplantation. Immunoblotting data demonstrated that the expression of GFAP, BDNF, neurotrophin-3 (NT3), and Bcl2 increased significantly in stem cell treated slices. This study illustrated that the fate of transplanted stem cells has been directed to the glial lineage in the ex vivo context of injury and EPI-NCSCs may ameliorate the SCI condition through releasing neurotrophic factors directly and/or via inducing resident spinal cord cells.

A hybrid conceptual cost estimating model using ANN and GA for power plant projects

Abstract: Providing an accurate completion cost estimate helps managers in deciding whether to undertake the project due to cash in hand. Hence, MAPNA Group Co. as an Iranian leading general contractor of power plant projects is not an exception too. Cost prediction in these projects is of great importance, whereas it can assist managers to keep their overall budget under control. Literature has been reviewed and influencing variables are explored. Thereafter, an artificial neural network model is developed and combined with genetic algorithm to select the best network architecture. According to the literature reviewed, almost all of the performed studies have selected the optimum network architecture through a process of trial and error, which makes the present method worthy of implementation. The best network architecture is capable of predicting projects’ cost of accuracy equal to 94.71%. A sensitivity analysis is then performed to test the significance degree of model input variables.