Neurology Commons^™

Method Of Intra-Arterial Drug Administration In A Rat: Sex Based Optimization Of Infusion Rate, Sarah J. Messmer, Justin F. Fraser, Keith R. Pennypacker, Jill M. Roberts

Center for Advanced Translational Stroke Science Faculty Publications

BACKGROUND: Endovascular thrombectomy is the process of removing a blood clot and re-establishing blood flow in patients with emergent large vessel occlusion. The technique provides an opportunity to deliver therapeutics directly to the site of injury. The intra-arterial (IA) route of drug administration in the mouse was developed to bridge the gap between animal stroke treatments and clinical stroke therapy. Here, we adapted the IA method for use in rats, by investigating various flow rates to optimize the IA injection through the internal carotid artery (ICA).

METHODS: Male and female Sprague-Dawley rats (∼4 months of age) were subjected to placement …

Recanalization Of The Chronically Occluded Internal Carotid Artery: Review Of The Literature., Mario Zanaty, Jorge A. Roa, Pascal Jabbour, Edgar A. Samaniego, David M. Hasan

Department of Neurosurgery Faculty Papers

Introduction: We reviewed the literature on interventions for patients with medically refractory chronically occluded internal carotid artery (COICA) to assess the risks and/or benefits after recanalization via an endovascular technique (ET) or hybrid surgery (HS, i.e., ET plus carotid endarterectomy).

Methods: A systematic search of the electronic databases was performed. Patients with COICA were classified into 4 different categories according to Hasan et al classification.

Results: Eighteen studies satisfied the inclusion criteria. Only 6 studies involved an HS procedure. We identified 389 patients with COICA who underwent ET or HS; 91% were males. The overall perioperative complication rate was 10.1% …

Eeg Artifact Removal Using A Wavelet Neural Network, Hoang-Anh T. Nguyen, John Musson, Jiang Li, Frederick Mckenzie, Guangfan Zhang, Roger Xu, Carl Richey, Tom Schnell, Thomas E. Pinelli (Ed.)

Electrical & Computer Engineering Faculty Publications

In this paper we developed a wavelet neural network. (WNN) algorithm for Electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural network and the time/frequency property of wavelet. We compared the WNN algorithm with the ICA technique and a wavelet thresholding method, which was realized by using the Stein's unbiased risk estimate (SURE) with an adaptive gradient-based optimal threshold. Experimental results on a driving test data set show that WNN can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy data.