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April 2012

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From:
"Diane St. Germain" <[log in to unmask]>
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Date:
Mon, 16 Apr 2012 11:11:43 -0400
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Dissertation Defense Announcement
To:  The George Mason University Community

Candidate: Michelle Raiszadeh
Program:    PhD Biosciences

Date:   Friday April 27, 2012
Time:   3:00 p.m.
Place:   George Mason University
             Bull Run Hall #246
             Prince William campus 
<http://www.gmu.edu/resources/visitors/findex.html>
 
Dissertation Director: Dr. Lance Liotta
Committee members: Dr. Emanuel Petricoin III, Dr. John Schreifels, Dr. 
Paul Russo
Title: "Proteomic Analysis of Eccrine Sweat: Implications for the 
Discovery of Schizophrenia Biomarker Proteins"

The dissertation is on reserve in the Johnson Center Library, Fairfax 
campus.
The doctoral project will not be read at the meeting, but should be read 
in advance.

All members of the George Mason University community are invited to attend.


*ABSTRACT:*

Although efforts have been made for over 20 years, there is currently no 
physical, clinical test available to confirm or diagnose schizophrenia 
(SZ).  For now, psychiatrists must rely on diagnosis based on clinical 
symptoms.  This requires an evaluation that takes several hours to 
perform.  No single symptom is definitive for diagnosis.  Instead, the 
diagnosis encompasses a pattern of signs and symptoms, along with 
impaired occupational or social functioning in accordance with the 
Diagnostic and Statistical Manual of Mental Disorders DSM-IV-TR.  Even 
with a thorough psychiatric evaluation, SZ is often misdiagnosed as 
Schizoid Personality, Schizophreniform Disorder, Schizotypal 
Personality, Bipolar Disorder (Manic Depression) which is also 
frequently misdiagnosed as SZ, and Asperger's Syndrome.  For this 
reason, many patients are prescribed medications for the wrong illness 
leading to exacerbation of the symptoms that they already have and 
possibly the addition of more symptoms that did not already exist.  
Current and past research efforts to produce a physical diagnostic test 
for SZ have focused on fluids such as blood and cerebral spinal fluid, 
both requiring invasive procedures for collection.  One fluid that has 
yet to be explored widely for potential diagnostic biomarkers is sweat.  
This fluid has been found to be a rich source of novel proteins that are 
also found to be differentiated in SZ patient sweat, and, thus, may hold 
the key for the first non-invasive, diagnostic test for SZ and pave the 
way for other non-invasive clinical diagnostic tests.

This dissertation describes the first ever, large-scale study of the 
eccrine sweat proteome which has been shown to have a vast population of 
proteins and peptides including those found to be differentiated in SZ 
versus controls.  This research focuses on the sweat proteome of those 
diagnosed with SZ and age-, race-, and gender-matched, healthy 
controls.  Differentiated proteins, as well as proteins found in both 
patients and controls at similar levels, were determined through 
analytical methods.  Liquid chromatography tandem mass spectrometry 
(LC-MS/MS) and multiple reaction monitoring mass spectrometry (MRM-MS) 
proteomics analyses were performed on eccrine sweat of healthy controls, 
and the results were compared with those from individuals diagnosed with 
SZ.  Seventeen proteins showed a differential abundance of approximately 
two-fold or greater between the SZ sample pool and the control sample 
pool.  This research demonstrates the utility of LC-MS/MS and MRM-MS as 
a viable strategy for the discovery and verification of potential sweat 
protein disease biomarkers.  This dissertation also includes a study to 
evaluate the reproducibility of eccrine sweat samples collected using 
the Webster sweat inducer and the MacroductTM sweat collector and 
relationships between SZ and selected proteins that were found to be 
differentially abundant between patients and controls.  These 
relationships were determined using the Database for Annotation, 
Visualization, and Integrated Discovery and the first schizophrenia 
molecular network (SMN) (Sun et al. 2010).

 ###



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