ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: Biomedical Engineering
Description
Specificity and affinity towards a given ligand/epitope limit target-specific delivery. Companies can spend between $500 million to $2 billion attempting to discover a new drug or therapy; a significant portion of this expense funds high-throughput screening to find the most successful target-specific compound available. A more recent addition to discovering highly specific targets is the application of phage display utilizing single chain variable fragment antibodies (scFv). The aim of this research was to employ phage display to identify pathologies related to traumatic brain injury (TBI), particularly astrogliosis. A unique biopanning method against viable astrocyte cultures activated with TGF-β achieved this aim. Four scFv clones of interest showed varying relative affinities toward astrocytes. One of those four showed the ability to identify reactive astroctyes over basal astrocytes through max signal readings, while another showed a statistical significance in max signal reading toward basal astrocytes. Future studies will include further affinity characterization assays. This work contributes to the development of targeting therapeutics and diagnostics for TBI.
ContributorsMarsh, William (Author) / Stabenfeldt, Sarah (Thesis advisor) / Caplan, Michael (Committee member) / Sierks, Michael (Committee member) / Arizona State University (Publisher)
Created2013
Description
Changes to the microenvironment of the endothelium can produce significant changes in the response of endothelial cells to stimuli. Human Aortic Endothelial Cells (HAECs) are tested in vitro for their fluid shear stress response when their substrates, and the solute concentrations of the fluids to which they are exposed, are modulated, and for their nitric oxide expression when they are exposed to hyperglycemic conditions. ImageJ is used to quantify either the degree of cellular alignment and elongation with the direction of flow, or the relative NO expression using the fluorochrome DAF-2. First, the results of Brower, et.al. are replicated: HAECs under normal glucose (4mM) conditions align and elongate with flow (p<<0.05), while high glucose (30.5mM) conditions negate this effect (p<<0.05) and is likely the result of Advanced Glycation End-products (AGEs). Then, in this study it is found that substitution of fibronectin for gelatin substrates does not impair flow (p<<0.05), indicating that fibronectin likely does not participate in the initiation of vascular lesions. High palmitic acid also does not prevent HAEC shear response (p<<0.05), which is consistent with Brower's predictions that AGEs are responsible for impaired elongation and alignment. NO production is significantly increased (p<<0.025) in HAECs cultured 24 hours under high glucose (30.5mM) conditions compared with normal glucose (4mM) conditions, indicating the presence of inducible nitric oxide as part of an inflammatory response. Aminoguanidine (5mM) added to high glucose concentrations reduces, but does not eliminate NO production (p<<0.05), likely due to insufficient concentration. Modulation of the endothelial microenvironment leads to pronounced changes in HAEC behavior with regards to NO production under hyperglycemic conditions. Diabetic model rat aortas are explanted and imaged for the purpose of detecting aortic endothelial cell alignment and elongation; improvements in this method are discussed. A microvessel chamber used with explanted human tissue is re-fit to reduce required volumes of solutions and allow more effective experimentation.
ContributorsLehnhardt, Eric (Author) / Caplan, Michael R (Thesis advisor) / Targovnik, Jerome (Committee member) / Sierks, Michael (Committee member) / Arizona State University (Publisher)
Created2013
Description
Gold nanoparticles as potential diagnostic, therapeutic and sensing systems have a long history of use in medicine, and have expanded to a variety of applications. Gold nanoparticles are attractive in biological applications due to their unique optical, chemical and biological properties. Particularly, gold nanorods (GNRs) are increasingly used due to superior optical property in the near infrared (NIR) window. Light absorbed by the nanorod can be dissipated as heat efficiently or re-emitted by the particle. However, the limitations for clinical translation of gold nanorods include low yields, poor stability, depth-restricted imaging, and resistance of cancer cells to hyperthermia, are severe. A novel high-throughput synthesis method was employed to significantly increase in yields of solid and porous gold nanorods/wires. Stable functional nanoassemblies and nanomaterials were generated by interfacing gold nanorods with a variety of polymeric and polypeptide-based coatings, resulting in unique properties of polymer-gold nanorod assemblies and composites. Here the use of these modified gold nanorods in a variety of applications including optical sensors, cancer therapeutics, and nanobiomaterials were described.
ContributorsHuang, Huang-Chiao (Author) / Rege, Kaushal (Thesis advisor) / Sierks, Michael (Committee member) / Dai, Lenore (Committee member) / Ramakrishna, B (Committee member) / Vogt, Bryan (Committee member) / Arizona State University (Publisher)
Created2012
Description
Semi-supervised learning (SSL) is sub-field of statistical machine learning that is useful for problems that involve having only a few labeled instances with predictor (X) and target (Y) information, and abundance of unlabeled instances that only have predictor (X) information. SSL harnesses the target information available in the limited labeled data, as well as the information in the abundant unlabeled data to build strong predictive models. However, not all the included information is useful. For example, some features may correspond to noise and including them will hurt the predictive model performance. Additionally, some instances may not be as relevant to model building and their inclusion will increase training time and potentially hurt the model performance. The objective of this research is to develop novel SSL models to balance data inclusivity and usability. My dissertation research focuses on applications of SSL in healthcare, driven by problems in brain cancer radiomics, migraine imaging, and Parkinson’s Disease telemonitoring.
The first topic introduces an integration of machine learning (ML) and a mechanistic model (PI) to develop an SSL model applied to predicting cell density of glioblastoma brain cancer using multi-parametric medical images. The proposed ML-PI hybrid model integrates imaging information from unbiopsied regions of the brain as well as underlying biological knowledge from the mechanistic model to predict spatial tumor density in the brain.
The second topic develops a multi-modality imaging-based diagnostic decision support system (MMI-DDS). MMI-DDS consists of modality-wise principal components analysis to incorporate imaging features at different aggregation levels (e.g., voxel-wise, connectivity-based, etc.), a constrained particle swarm optimization (cPSO) feature selection algorithm, and a clinical utility engine that utilizes inverse operators on chosen principal components for white-box classification models.
The final topic develops a new SSL regression model with integrated feature and instance selection called s2SSL (with “s2” referring to selection in two different ways: feature and instance). s2SSL integrates cPSO feature selection and graph-based instance selection to simultaneously choose the optimal features and instances and build accurate models for continuous prediction. s2SSL was applied to smartphone-based telemonitoring of Parkinson’s Disease patients.
The first topic introduces an integration of machine learning (ML) and a mechanistic model (PI) to develop an SSL model applied to predicting cell density of glioblastoma brain cancer using multi-parametric medical images. The proposed ML-PI hybrid model integrates imaging information from unbiopsied regions of the brain as well as underlying biological knowledge from the mechanistic model to predict spatial tumor density in the brain.
The second topic develops a multi-modality imaging-based diagnostic decision support system (MMI-DDS). MMI-DDS consists of modality-wise principal components analysis to incorporate imaging features at different aggregation levels (e.g., voxel-wise, connectivity-based, etc.), a constrained particle swarm optimization (cPSO) feature selection algorithm, and a clinical utility engine that utilizes inverse operators on chosen principal components for white-box classification models.
The final topic develops a new SSL regression model with integrated feature and instance selection called s2SSL (with “s2” referring to selection in two different ways: feature and instance). s2SSL integrates cPSO feature selection and graph-based instance selection to simultaneously choose the optimal features and instances and build accurate models for continuous prediction. s2SSL was applied to smartphone-based telemonitoring of Parkinson’s Disease patients.
ContributorsGaw, Nathan (Author) / Li, Jing (Thesis advisor) / Wu, Teresa (Committee member) / Yan, Hao (Committee member) / Hu, Leland (Committee member) / Arizona State University (Publisher)
Created2019
Description
Traumatic brain injury (TBI) affects an estimated 1.7 million people in the United States each year and is a leading cause of death and disability for children and young adults in industrialized countries. Unfortunately, the molecular and cellular mechanisms of injury progression have yet to be fully elucidated. Consequently, this complexity impacts the development of accurate diagnosis and treatment options. Biomarkers, objective signatures of injury, can inform and facilitate development of sensitive and specific theranostic devices. Discovery techniques that take advantage of mining the temporal complexity of TBI are critical for the identification of high specificity biomarkers.
Domain antibody fragment (dAb) phage display, a powerful screening technique to uncover protein-protein interactions, has been applied to biomarker discovery in various cancers and more recently, neurological conditions such as Alzheimer’s Disease and stroke. The small size of dAbs (12-15 kDa) and ability to screen against brain vasculature make them ideal for interacting with the neural milieu in vivo. Despite these characteristics, implementation of dAb phage display to elucidate temporal mechanisms of TBI has yet to reach its full potential.
My dissertation employs a unique target identification pipeline that entails in vivo dAb phage display and next generation sequencing (NGS) analysis to screen for temporal biomarkers of TBI. Using a mouse model of controlled cortical impact (CCI) injury, targeting motifs were designed based on the heavy complementarity determining region (HCDR3) structure of dAbs with preferential binding to acute (1 day) and subacute (7 days) post-injury timepoints. Bioreactivity for these two constructs was validated via immunohistochemistry. Further, immunoprecipitation-mass spectrometry analysis identified temporally distinct candidate biological targets in brain tissue lysate.
The pipeline of phage display followed by NGS analysis demonstrated a unique approach to discover motifs that are sensitive to the heterogeneous and diverse pathology caused by neural injury. This strategy successfully achieves 1) target motif identification for TBI at distinct timepoints and 2) characterization of their spatiotemporal specificity.
Domain antibody fragment (dAb) phage display, a powerful screening technique to uncover protein-protein interactions, has been applied to biomarker discovery in various cancers and more recently, neurological conditions such as Alzheimer’s Disease and stroke. The small size of dAbs (12-15 kDa) and ability to screen against brain vasculature make them ideal for interacting with the neural milieu in vivo. Despite these characteristics, implementation of dAb phage display to elucidate temporal mechanisms of TBI has yet to reach its full potential.
My dissertation employs a unique target identification pipeline that entails in vivo dAb phage display and next generation sequencing (NGS) analysis to screen for temporal biomarkers of TBI. Using a mouse model of controlled cortical impact (CCI) injury, targeting motifs were designed based on the heavy complementarity determining region (HCDR3) structure of dAbs with preferential binding to acute (1 day) and subacute (7 days) post-injury timepoints. Bioreactivity for these two constructs was validated via immunohistochemistry. Further, immunoprecipitation-mass spectrometry analysis identified temporally distinct candidate biological targets in brain tissue lysate.
The pipeline of phage display followed by NGS analysis demonstrated a unique approach to discover motifs that are sensitive to the heterogeneous and diverse pathology caused by neural injury. This strategy successfully achieves 1) target motif identification for TBI at distinct timepoints and 2) characterization of their spatiotemporal specificity.
ContributorsMartinez, Briana Isabella (Author) / Stabenfeldt, Sarah E (Thesis advisor) / Lifshitz, Jonathan (Committee member) / Sierks, Michael (Committee member) / Kleim, Jeffrey (Committee member) / Arizona State University (Publisher)
Created2020