Seyed Mohammad Hosseini

Delivery of Epidermal Neural Crest Stem Cells (EPI-NCSC) to hippocamp in Alzheimer's Disease Rat Model

BACKGROUND Alzheimers disease (AD) is characterized by progressive neuronal loss in hippocamp. Epidermal neural crest stem cells (EPI-NCSC) can differentiate into neurons, astrocytes and oligodendrocytes. The purpose of this study was to evaluate the effects of transplanting EPI-NCSC into AD rat model. METHODS Two weeks after induction of AD by injection of Amyloid-β1-40 into CA1 area of rat hippocamp, Y-maze and single-trial passive avoidance tests were used to show deficit of learning and memory abilities. EPI-NCSC were obtained from the vibrissa hair follicle of rat, cultured and labeled with bromodeoxyuridine. When Alzheimer was proved by behavioral tests, EPI-NCSC was transplanted into CA3 area of hippocamp in AD rat model. The staining of EPI-NCSC markers (nestin and SOX10) was done in vitro. Double-labeling immunofluorescence was performed to study survival and differentiation of the grafted cells. RESULTS We showed that transplanted EPI-NCSC survive and produce many neurons and a few glial cells, presenting glial fibrillary acidic protein. Total number of granule cells in hippocamp was estimated to be more in the AD rat model with transplanted cells as compared to AD control group. We observed that rats with hippocampal damage made more errors than control rats on the Y-maze, when reward locations were reversed. CONCLUSION Transplanted cells were migrated to all areas of hippocamp and the total number of granule cell in treatment group was equal compared to control group. Transplantation of EPI-NCSC into hippocamp might differentiate into cholinergic neurons and could cure impairment of memory in AD rat model.

Day-ahead price forecasting based on hybrid prediction model

Short-Term Price Forecast is a key issue for operation of both regulated power systems and electricity markets. Energy price forecast is the key information for generating companies to prepare their bids in the electricity markets. However, this forecasting problem is complex due to nonlinear, nonstationary, and time variant behavior of electricity price time series. So, in this article, the forecast model includes wavelet transform, autoregressive integrated moving average, and radial basis function neural networks (RBFN) is presented. Also, an intelligent algorithm is applied to optimize the RBFN structure, which adapts it to the specified training set, reduce computational complexity and avoids over fitting. Effectiveness of the proposed method is applied for price forecasting of electricity market of mainland Spain and its results are compared with the results of several other price forecast methods. These comparisons confirm the validity of the developed approach.

Statistical Analysis of Cognitive Radio Operation in a Periodic Pattern of Sensing and Transmission

A Cognitive Radio must sense the channel to detect spectrum holes. To this end, it senses the channel for

Available and Waiting Times for Cognitive Radios

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Adult neurogenesis of epidermal neural crest stem cells (EPI-NCSC) in hippocampus of Alzheimer's rat model

TS and transmits its data for

First Report of Rhipicephalus turanicus from Hedgehog (Erinaceus concolor) in North of Iran

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