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- Genre: Doctoral Dissertation
Description
Current treatment methods for cerebral aneurysms are providing life-saving measures for patients suffering from these blood vessel wall protrusions; however, the drawbacks present unfortunate circumstances in the invasive procedure or with efficient occlusion of the aneurysms. With the advancement of medical devices, liquid-to-solid gelling materials that could be delivered endovascularly have gained interest. The development of these systems stems from the need to circumvent surgical methods and the requirement for improved occlusion of aneurysms to prevent recanalization and potential complications. The work presented herein reports on a liquid-to-solid gelling material, which undergoes gelation via dual mechanisms. Using a temperature-responsive polymer, poly(N-isopropylacrylamide) (poly(NIPAAm), the gelling system can transition from a solution at low temperatures to a gel at body temperature (physical gelation). Additionally, by conjugating reactive functional groups onto the polymers, covalent cross-links can be formed via chemical reaction between the two moieties (chemical gelation). The advantage of this gelling system comprises of its water-based properties as well as the ability of the physical and chemical gelation to occur within physiological conditions. By developing the polymer gelling system in a ground-up approach via synthesis, its added benefit is the capability of modifying the properties of the system as needed for particular applications, in this case for embolization of cerebral aneurysms. The studies provided in this doctoral work highlight the synthesis, characterization and testing of these polymer gelling systems for occlusion of aneurysms. Conducted experiments include thermal, mechanical, structural and chemical characterization, as well as analysis of swelling, degradation, kinetics, cytotoxicity, in vitro glass models and in vivo swine study. Data on thermoresponsive poly(NIPAAm) indicated that the phase transition it undertakes comes as a result of the polymer chains associating as temperature is increased. Poly(NIPAAm) was functionalized with thiols and vinyls to provide for added chemical cross-linking. By combining both modes of gelation, physical and chemical, a gel with reduced creep flow and increased strength was developed. Being waterborne, the gels demonstrated excellent biocompatibility and were easily delivered via catheters and injected within aneurysms, without undergoing degradation. The dual gelling polymer systems demonstrated potential in use as embolic agents for cerebral aneurysm embolization.
ContributorsBearat, Hanin H (Author) / Vernon, Brent L (Thesis advisor) / Frakes, David (Committee member) / Massia, Stephen (Committee member) / Pauken, Christine (Committee member) / Preul, Mark (Committee member) / Solis, Francisco (Committee member) / Arizona State University (Publisher)
Created2012
Description
Cigarette smoking remains a major global public health issue. This is partially due to the chronic and relapsing nature of tobacco use, which contributes to the approximately 90% quit attempt failure rate. The recent rise in mobile technologies has led to an increased ability to frequently measure smoking behaviors and related constructs over time, i.e., obtain intensive longitudinal data (ILD). Dynamical systems modeling and system identification methods from engineering offer a means to leverage ILD in order to better model dynamic smoking behaviors. In this dissertation, two sets of dynamical systems models are estimated using ILD from a smoking cessation clinical trial: one set describes cessation as a craving-mediated process; a second set was reverse-engineered and describes a psychological self-regulation process in which smoking activity regulates craving levels. The estimated expressions suggest that self-regulation more accurately describes cessation behavior change, and that the psychological self-regulator resembles a proportional-with-filter controller. In contrast to current clinical practice, adaptive smoking cessation interventions seek to personalize cessation treatment over time. An intervention of this nature generally reflects a control system with feedback and feedforward components, suggesting its design could benefit from a control systems engineering perspective. An adaptive intervention is designed in this dissertation in the form of a Hybrid Model Predictive Control (HMPC) decision algorithm. This algorithm assigns counseling, bupropion, and nicotine lozenges each day to promote tracking of target smoking and craving levels. Demonstrated through a diverse series of simulations, this HMPC-based intervention can aid a successful cessation attempt. Objective function weights and three-degree-of-freedom tuning parameters can be sensibly selected to achieve intervention performance goals despite strict clinical and operational constraints. Such tuning largely affects the rate at which peak bupropion and lozenge dosages are assigned; total post-quit smoking levels, craving offset, and other performance metrics are consequently affected. Overall, the interconnected nature of the smoking and craving controlled variables facilitate the controller's robust decision-making capabilities, even despite the presence of noise or plant-model mismatch. Altogether, this dissertation lays the conceptual and computational groundwork for future efforts to utilize engineering concepts to further study smoking behaviors and to optimize smoking cessation interventions.
ContributorsTimms, Kevin Patrick (Author) / Rivera, Daniel E (Thesis advisor) / Frakes, David (Committee member) / Nielsen, David R (Committee member) / Arizona State University (Publisher)
Created2014