In 2015, American Heart Association (AHA) reported that 1 in 9 deaths are attributed to Heart failure (HF), the number one killer in the world. While advancements in interventional cardiology in conjunction with pharmacotherapies have significantly reduced the rate of mortality following MI, there has been a corresponding rise in chronic heart failure (CHF) in surviving patients, largely attributed to the limited regenerative capacity of the heart and the inadequate healing response. Myocardial ischemic injury triggers an exuberant local and systemic inflammation, and the extent and quality of the cardiac wound healing process is intricately tied to the delicate equilibrium of this inflammatory response. While cardiac regeneration is an important goal, it is imperative in the meantime to explore therapeutic strategies that target these inflammatory mediators of early cardiac repair. These interventions to influence and improve cardiac wound healing can represent a new therapeutic window to halt the progression of heart failure between the few hours that may be used to limit infarct size by reperfusion and an irreversible non-contractile cardiac scar. This dissertation examines three therapeutic delivery strategies aimed at modulating the immune response to enhance cardiac repair in rodent models MI: 1) Polyketal nanoparticles as siRNA delivery vehicles for antioxidant therapy; 2) Spherical nucleic acid particles for anti-inflammatory therapy and; 3) Bioactive PEG (polyethyleneglycol)-based hydrogel for immunomodulation. The work presented here applies novel nucleic acid delivery strategies for cardiac gene silencing and has contributed to new knowledge with regard to modulating the immune response following MI.
Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/53854 |
Date | 21 September 2015 |
Creators | Somasuntharam, Inthirai |
Contributors | Davis, Michael, García, Andrés, Taylor, Robert, Prausnitz, Mark, Denning, Timothy, Davis, Michael |
Publisher | Georgia Institute of Technology |
Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
Language | en_US |
Detected Language | English |
Type | Dissertation |
Format | application/pdf |
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