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Azhar Rafiq

Researcher at Virginia Commonwealth University

Publications -  50
Citations -  1864

Azhar Rafiq is an academic researcher from Virginia Commonwealth University. The author has contributed to research in topics: Telemedicine & Status epilepticus. The author has an hindex of 21, co-authored 50 publications receiving 1737 citations. Previous affiliations of Azhar Rafiq include VCU Medical Center.

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A Neuronal Glutamate Transporter Contributes to Neurotransmitter GABA Synthesis and Epilepsy

TL;DR: There was a 50% loss of hippocampal GABA levels associated with knockdown ofEAAC1, and newly synthesized GABA from extracellular glutamate was significantly impaired by reduction of EAAC1 expression, indicating decreased tonic inhibition.
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Generation and propagation of epileptiform discharges in a combined entorhinal cortex/hippocampal slice.

TL;DR: The development of epileptiform discharges in response to tetanic stimulation of the Schaeffer collaterals was studied by using extracellular field potential recordings in CA1, CA3, dentate gyrus, and entorhinal cortex and intracellular recordings inCA1 neurons in rat hippocampal-parahippocampal slices, which were cut so as to maintain reciprocal connections between entorHinal cortical and hippocampus in vitro.
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Long-lasting reduction of inhibitory function and gamma-aminobutyric acid type A receptor subunit mRNA expression in a model of temporal lobe epilepsy

TL;DR: Results indicate that selective, long-lasting reduction of GABAA subunit mRNA expression and increased excitability, possibly reflecting loss of GABAergic inhibition, occur in an in vivo model of partial complex epilepsy.
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Long-term alteration of calcium homeostatic mechanisms in the pilocarpine model of temporal lobe epilepsy

TL;DR: It is suggested that altered [Ca(2+)](i) homeostatic mechanisms may underlie some aspects of the epileptic phenotype and contribute to the persistent neuroplasticity changes associated with epilepsy.
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Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks

TL;DR: A novel approach that uses ConvNet for classifying brain medical images into healthy and unhealthy brain images, using the modified version of the Alex Krizhevsky network (AlexNet) deep learning architecture on magnetic resonance images as a potential tumor classification technique.