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Detection Of Lou Gehrig?s Disease And Parkinson?s Disease In Rat Models Through Locomotion Analysis | 4918
ISSN: 2155-952X

Journal of Biotechnology & Biomaterials
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Detection of Lou Gehrig?s disease and parkinson?s disease in rat models through locomotion analysis

3rd World Congress on Biotechnology

Wenlong Tang and Uri Tasch

AcceptedAbstracts: J Biotechnol Biomater

DOI: 10.4172/2155-952X.S1.021

A non-invasive locomotion analysis approach is described to detect locomotion deficiencies due to amyotrophic lateral sclerosis (ALS) (also called Lou Gehrig?s disease) and Parkinson?s disease (PD) in laboratory rats. SOD1-G93A and 6-OHDA lesioned rats were used in the experiments to model ALS and PD, respectively. The locomotion data was collected by an in house locomotion analysis system introduced in our previous publications. The locomotion deficiencies are recognized by performing three different comparisons: (i) comparing the locomotion of an ALS model, G93A mutation of SOD1 with control rats, (ii) comparing the locomotion of a PD model, 6-OHDA lesioned with control rats, and (iii) comparing the locomotion of G93A/ SOD1 and 6-OHDA lesioned rats. Each comparison resulted in different set of locomotion parameters (LPs) for ALS and PD that characterized the locomotion deficiencies and resulted in the best logistic regression model that classifies the rats into diseased/ healthy groups with minimum error. All the LPs that best capture the locomotion differences in the first two comparisons and two out of three best LPs in the third comparison are derived from Ground Reaction Forces (GRFs), indicating the importance of measuring GRFs components in locomotion characterizing. The sensitivity and the specificity of the classification for comparisons (i), (ii), and (iii) were all above 90%. The proposed approach may potentially assist the diagnosis of neurological disorders in the clinical practice.
Wenlong Tang is a postdoctoral fellow at The University of Alabama. His research interests include locomotion analysis, signal processing and pattern recognition. He developed a noninvasive early detection methodology based on locomotion analysis for neurological and neuromuscular disorders. He has published more than 20 papers in journals and conference proceedings. Moreover, he is the co-inventor of a pending US patent and two Chinese patents. He received his Ph.D. in Mechanical Engineering from University of Maryland Baltimore County in 2010. He is serving as an editorial board member of Journal of Medical Advancements in Genetic Engineering.