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- All Subjects: Biomedical Engineering
- Genre: Doctoral Dissertation
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
Description
Alzheimer's disease (AD) is the most common form of dementia leading to cognitive dysfunction and memory loss as well as emotional and behavioral disorders. It is the 6th leading cause of death in United States, and the only one among top 10 death causes that cannot be prevented, cured or slowed. An estimated 5.4 million Americans live with AD, and this number is expected to triple by year 2050 as the baby boomers age. The cost of care for AD in the US is about $200 billion each year. Unfortunately, in addition to the lack of an effective treatment or AD, there is also a lack of an effective diagnosis, particularly an early diagnosis which would enable treatment to begin before significant neuronal damage has occurred.
Increasing evidence implicates soluble oligomeric forms of beta-amyloid and tau in the onset and progression of AD. While many studies have focused on beta-amyloid, soluble oligomeric tau species may also play an important role in AD pathogenesis. Antibodies that selectively identify and target specific oligomeric tau variants would be valuable tools for both diagnostic and therapeutic applications and also to study the etiology of AD and other neurodegenerative diseases.
Recombinant human tau (rhTau) in monomeric, dimeric, trimeric and fibrillar forms were synthesized and purified to perform LDH assay on human neuroblastoma cells, so that trimeric but not monomeric or dimeric rhTau was identified as extracellularly neurotoxic to neuronal cells. A novel biopanning protocol was designed based on phage display technique and atomic force microscopy (AFM), and used to isolate single chain antibody variable domain fragments (scFvs) that selectively recognize the toxic tau oligomers. These scFvs selectively bind tau variants in brain tissue of human AD patients and AD-related tau transgenic rodent models and have potential value as early diagnostic biomarkers for AD and as potential therapeutics to selectively target toxic tau aggregates.
Increasing evidence implicates soluble oligomeric forms of beta-amyloid and tau in the onset and progression of AD. While many studies have focused on beta-amyloid, soluble oligomeric tau species may also play an important role in AD pathogenesis. Antibodies that selectively identify and target specific oligomeric tau variants would be valuable tools for both diagnostic and therapeutic applications and also to study the etiology of AD and other neurodegenerative diseases.
Recombinant human tau (rhTau) in monomeric, dimeric, trimeric and fibrillar forms were synthesized and purified to perform LDH assay on human neuroblastoma cells, so that trimeric but not monomeric or dimeric rhTau was identified as extracellularly neurotoxic to neuronal cells. A novel biopanning protocol was designed based on phage display technique and atomic force microscopy (AFM), and used to isolate single chain antibody variable domain fragments (scFvs) that selectively recognize the toxic tau oligomers. These scFvs selectively bind tau variants in brain tissue of human AD patients and AD-related tau transgenic rodent models and have potential value as early diagnostic biomarkers for AD and as potential therapeutics to selectively target toxic tau aggregates.
ContributorsTian, Huilai (Author) / Sierks, Michael R (Thesis advisor) / Dai, Lenore (Committee member) / Tillery, Stephen H (Committee member) / Nielsen, David R (Committee member) / Stabenfeldt, Sarah (Committee member) / Arizona State University (Publisher)
Created2014
Description
This dissertation provides a fundamental understanding of the properties of mesoporous carbon based materials and the utilization of those properties into different applications such as electrodes materials for super capacitors, adsorbents for water treatments and biosensors. The thickness of mesoporous carbon films on Si substrates are measured by Ellipsometry method and pore size distribution has been calculated by Kelvin equation based on toluene adsorption and desorption isotherms monitored by Ellipsometer. The addition of organometallics cobalt and vanalyl acetylacetonate in the synthesis precursor leads to the metal oxides in the carbon framework, which largely decreased the shrink of the framework during carbonization, resulting in an increase in the average pore size. In addition to the structural changes, the introduction of metal oxides into mesoporous carbon framework greatly enhances the electrochemical performance as a result of their pseudocapacitance. Also, after the addition of Co into the framework, the contraction of mesoporous powders decreased significantly and the capacitance increased prominently because of the solidification function of CoO nanoparticles. When carbon-cobalt composites are used as adsorbent, the adsorption capacity of dye pollutant in water is remarkably higher (90 mg/g) after adding Co than the mesoporous carbon powder (2 mg/g). Furthermore, the surface area and pore size of mesoporous composites can be greatly increased by addition of tetraethyl orthosilicate into the precursor with subsequent etching, which leads to a dramatic increase in the adsorption capacity from 90 mg/g up to 1151 mg/g. When used as electrode materials for amperometric biosensors, mesoporous carbons showed good sensitivity, selectivity and stability. And fluorine-free and low-cost poly (methacrylate)s have been developed as binders for screen printed biosensors. With using only 5wt% of poly (hydroxybutyl methacrylate), the glucose sensor maintained mechanical integrity and exhibited excellent sensitivity on detecting glucose level in whole rabbit blood. Furthermore, extremely high surface area mesoporous carbons have been synthesized by introducing inorganic Si precursor during self-assembly, which effectively determined norepinephrine at very low concentrations.
ContributorsDai, Mingzhi (Author) / Vogt, Bryan D (Thesis advisor) / La Belle, Jeffrey T (Committee member) / Dai, Lenore (Committee member) / Nielsen, David R (Committee member) / Torres, César I (Committee member) / Arizona State University (Publisher)
Created2012