Roy M Smeal

 

Roy M Smeal

Title:

Research Assistant Professor


Email:

Roy.Smeal@m.cc.utah.edu



Education:

 

B.S. Electrical Engineering,1997,  Texas A&M University, College Station, TX.

Ph.D. Bioengineering, 2004, University of Utah, Salt Lake City, UT.

Postdoctoral Fellow, Pharmacology and Toxicology,2004-2007, University of Utah, Salt Lake City, UT.




 

Research Interests

 

I am interested in utilizing animal models of nervous system diseases to help understand the function of affected brain regions both in normal and pathological conditions.  Currently I am studying two different animal models: a 6-OHDA-lesion rat model of Parkinson's disease to examine striatal function and a novel virus-induced mouse model of epilepsy to examine hippocampal function.  In vitro electrophysiological, imaging, and pharmacological techniques are used to discover the synaptic and neuron-intrinsic changes that occur in the disease models.  These changes, once correlated with the pathological state, can be used to infer about normal neural function and how this function might change in the pathological state.  Hypotheses about the nature of the change that potentially causes the pathology can then be tested by experimentally inserting a “synthetic” version of the observed change, such as changes in type or amount of ligand- and voltage-gated channels, into a healthy neuron using the dynamic patch clamp technique, an experimental technique that allows the altering of the electrophysiological characteristics of neurons in real time.  If the hypothesized change is a cause of the pathology, the experimental insertion of the electrophysiological change should produce the other electrophysiological characteristics of the pathology in the neuron examined. This experimental strategy has the dual benefit of utilizing animal models for the basic scientific endeavor of understanding normal brain function, while simultaneously advancing the goal of identifying potential drug targets to treat these diseases.




 

Selected Publications

 

Webb, K., Li, W., Hitchcock, R.W., Smeal, R.M. , Gray, S.D., and Tresco, P.A. Comparison of human fibroblast ECM-related gene expression on elastic three-dimensional substrates relative to two-dimensional films of the same material. Biomaterials, 24, 4681-4690, 2003.

Smeal, R.M. , Rabbitt, R., Biran, R., and Tresco, P.A. Substrate curvature influences the direction of nerve outgrowth. Annals of Biomedical Engineering, 33, 376-382, 2005.

Webb, K., Hitchcock, R.W., Smeal, R.M. , Li, W., Gray, S.D., and Tresco, P.A. Cyclic strain increases fibroblast proliferation, matrix accumulation, and elastic modulus of fibroblast seeded polyurethane constructs. Journal of Biomechanics, 39, 1136-1144, 2006.

Smeal, R.M., Gaspar, R.C., Keefe, K.A., and Wilcox, K.S. A rat brain slice preparation for characterizing both thalamostriatal and corticostriatal afferents. Journal of Neuroscience Methods, 159, 224-235, 2007.

Smeal, R.M. and Tresco, P.A. The Influence of Substrate Curvature on Neurite Outgrowth is Cell Type Dependent. Experimental Neurology, 213: 281-92, 2008.

Smeal, R.M., Keefe, K.A., and Wilcox, K.S. Differences in excitatory transmission between thalamic and cortical afferents to single spiny efferent neurons of dorsal striatum. European Journal of Neuroscience, 28: 2041-52, 2008.

Smeal, R.M., Ermentrout, G.B., and White, J.A. Phase response curves and synchronized neural networks. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, (accepted), 2009.

Smeal, R.M., Ermentrout G.B., and White J.A. Phase-response curves and synchronized neural networks. Philos Trans R Soc Lond B Biol Sci., 365: 2407-22, 2010. PMID: 20603361

Smeal, R.M., Stewart, K.A., Iacob, E., Fujinami, R.S., White, H.S., and Wilcox, K.S., Changing properties of CA3 excitatory network activity in Theiler's virus-infected mice during disease progression. Journal of Neurovirology, 18:30-44, 2012. PMID: 22328242

Smeal, R.M., Economo M.N., Lillis K.P., Wilcox K.S., and White J.A., Targeted Path Scanning: An emerging method for recording fast changing network dynamics across large distances. Journal of Bioengineering & Biomedical Science, in press.

 

 

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