Tehran University Medical Journal

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Gliomas include a group of primary central nervous system (CNS) neoplasms with characteristics of neuroglial cells (eg, astrocytes, oligodendrocytes). The gliomas are classified commonly to WHO grade I-IV gliomas. The grading is based on the presence of nuclear atypia, vascular proliferation, mitoses, and necrosis. The malignant gliomas are progressive brain tumors that are divided into anaplastic gliomas and glioblastoma based upon their histopathologic features. Today, different modalities such as surgery, radiation therapy (in the form of external beam radiation or the stereotactic approach using radiosurgery) and chemotherapy have been used for the treatment of gliom's tomors but unfortunately the prognosis and survival rate is poor in most of patients. The survival depends on the tumor's type, size, location and the patient's age. We reviewed the prognostic factors, diagnostic modalities and surgical management of patients with gliomas.

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Normal 0 false false false EN-US X-NONE AR-SA MicrosoftInternetExplorer4 Background: Skin-derived precursors (SKPs) are a type of progenitor cells extracted from mammalian dermal tissue and can be differentiate to neural and mesodermal lineage in vitro. These cells can introduce an accessible autologos source of neural precursor cells for treatment of different neurodegenerative diseases. This research was done in order to set up isolation, culture, proliferation and differentiation of human skin derived precursors (hSKPs).
Methods: Human foreskin samples were cut into smaller pieces and cultured in proliferation medium after enzymatic digestion. To induce neural differentiation, cells were cultured in neural differentiation medium after fifth passage. We used immunocytochemistry and RT-PCR for characterization of the cells. Neuron and glial cell differentiation potential was assessed by immunofloresence using specific antibodies. The experiments were carried out in triplicate.
Results: After differentiation, βΙΙΙ- tubulin and neurofilament-M positive cells were observed that are specific markers for neurons. Moreover, glial fibrillary acid protein (GFAP) and S100 positive cells were identified that are markers specifically express in glial cells. Detected neurons and glials were also confirmed by their morphologic characterizations.
Conclusion: Our results demonstrated that skin-derived precursors obtained from human foreskin can exhibit neuronal and glial differentiation potential in vitro, depending on the protocols of induction.

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Background: Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort neuralspikes automatically. However, sorting neural spikes is a challenging task because of the low signal to noise ratio (SNR) of the spikes. The main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system.

Methods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF) neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used.

Results: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS) reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity.

Conclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.

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The recording of electrophysiological activities of brain neurons in the last half-century has been considered as one of the effective tools for the development of neuroscience. One of the techniques for recording the activity of nerve cells is the multi-electrode arrays (MEAs). Microelectrode arrays (MEAs) are usually employed to record electrical signals from electrogenic cells like neurons or cardiomyocytes. MEAs consist of an array of planar or three-dimensional electrodes that act as electrical interfaces and record cellular signals or stimulate cells. These platforms can be used in different applications including neuroscience studies, prostheses and rehabilitation, deep brain stimulation (DBS), cardiac pacemakers, retinal and cochlear implants, or for brain-computer interfaces (BCI) in general. Multi-electrode arrays are known as long-term recording and non-invasive devices. The MEA structure includes arrays of electrodes with micrometer and nanometer dimensions which are designed to stimulate and record the electrical activity of cells, and are fabricated using micromachining technologies. MEAs should be biocompatible to serve as a substrate for cell growth. On the other hand, they must have low impedance to be able to provide a high signal-to-noise ratio, and small size to offer a suitable spatial resolution for recording. MEAs are usually fabricated on glass substrates patterned with high-conductivity metals such as gold, iridium or platinum, which are insulated with a biocompatible layer. Despite fast progress, current multi-electrode arrays for neural applications still face limitations such as low signal-to-noise ratio and spatial resolution. To achieve better spatial resolution and lower noise levels and therefore more accurate signal, it is necessary to develop arrays with smaller sizes and lower impedance. Meanwhile, many nanostructures such as graphene, carbon nanotubes, gold nanoparticles, and also conductive polymers have become attractive candidates for this application due to their interesting properties. In this paper, the technology of multi-electrode arrays, how it works and its various parts are introduced, and finally, the challenges and developments in this field are investigated. Multi-electrode array technology is used for neuroscience research, neural network analysis, drug effects screening, and neural prosthesis studies.