Title:
High Shear Arterial Thrombosis: Microfluidic Diagnostics and Nanotherapeutics
High Shear Arterial Thrombosis: Microfluidic Diagnostics and Nanotherapeutics
Author(s)
Griffin, Michael T.
Advisor(s)
Ku, David N.
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Abstract
Ischemic cardiovascular events remain the leading causes of death in the world, largely due to ineffective preventative therapies and diagnostic tools. This work investigated the development of a physiologically relevant, low-variability microfluidic thrombosis assay (MTA) capable of screening therapy efficacy. First, an experimental design was implemented to assess the effects of geometry, collagen surface coverage, and anticoagulant selection on MTA occlusion time (OT) variability. It was found that better control of shear rates through novel grayscale lithography techniques decreased OT variability. Fibrillar collagens was also found to have a significant impact. The MTA was then implemented to study the effects of current antiplatelet therapies, aspirin and Plavix, as compared to the endpoints of other platelet function tests (PFTs). It was found that aspirin use significantly increased MTA OT but did not prevent occlusion in the MTA. Results from Plavix use found a stronger response, where 20% of patients had complete OT inhibition. Comparison with other PFTs found that the MTA more closely matched the rates of ischemic events from larger clinical studies. Finally, the MTA was utilized to assess a nanoparticle therapy hypothesized to function through biophysical mechanisms. It was found that negatively charged nanoparticles were more effective than neutral or positively charged nanoparticles. The antithrombotic effect of charged nanoparticles persisted even with different base materials, but the effects of nanoparticle size were not consistent between materials. A mouse bleeding model was also used to show that hemostasis was maintained with the nanoparticle therapy. The implications of all results for clinical diagnostic and future antithrombotic therapy research are discussed.
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Date Issued
2019-12-20
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Resource Type
Text
Resource Subtype
Dissertation