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1	TARGETABLE MULTI-DRUG NANOPARTICLES FOR TREATMENT OF GLIOBLASTOMA WITH NEUROIMAGING ASSESSMENT Shelby Brentyn Smiley (8786417) 01 May 2020 (has links) Glioblastoma (GBM) is a deadly, malignant brain tumor with a poor long-term prognosis. The current median survival is approximately fifteen to seventeen months with the standard of care therapy which includes surgery, radiation, and chemotherapy. An important factor contributing to recurrence of GBM is high resistance of GBM cancer stem cells (CSCs), for which a systematically delivered single drug approach will be unlikely to produce a viable cure. Therefore, multi-drug therapies are needed. Currently, only temozolomide (TMZ), which is a DNA alkylator, affects overall survival in GBM patients. CSCs regenerate rapidly and over-express a methyl transferase which overrides the DNA-alkylating mechanism of TMZ, leading to drug resistance. Idasanutlin (RG7388, R05503781) is a potent, selective MDM2 antagonist that additively kills GBM CSCs when combined with diagnostics in a truly theranostic manner for enhancing personalized medicine against GBM. The goal of this thesis was to develop a multi-drug therapy using mutli-functional nanoparticles (NPs) that preferentially target the GBM CSC subpopulation and provide in vivo preclinical imaging capability. Polymer-micellar NPs composed of poly(styrene-<i>b</i>-ethylene oxide) (PS-<i>b</i>-PEO) and poly(lactic-<i>co</i>-glycolic) acid (PLGA) were developed investigating both single and double emulsion fabrication techniques as well as combinatinos of TMZ and RG7388. The NPs were covalently bound to a 15 base-pair CD133 aptamer in order to target a specific epitope on the CD133 antigen expressed on the surface of GBM CSC subpopulation. For theranostic functionality, the NPs were also labelled with a positron emission tomography (PET) radiotracer, zirconium-89 (<sup>89</sup>Zr). The NPs maintained a small size of less than 100 nm, a relatively neutral charge and exhibited the ability to produce a cytotoxic effect on CSCs. There was a slight increase in killing with the aptamer-bound NPs compared to those without a targeting agent. This work has provided a potentially therapeutic option for GBM specific for CSC targeting and future in vivo biodistribution Nanoparticles Cancer Cancers Targeted Delivery theranostic delivery Cancer Stem Cells Cells
2	EXPLORING GRAPH NEURAL NETWORKS FOR CLUSTERING AND CLASSIFICATION Fattah Muhammad Tahabi (14160375) 03 February 2023 (has links) <p><strong>Graph Neural Networks</strong> (GNNs) have become excessively popular and prominent deep learning techniques to analyze structural graph data for their ability to solve complex real-world problems. Because graphs provide an efficient approach to contriving abstract hypothetical concepts, modern research overcomes the limitations of classical graph theory, requiring prior knowledge of the graph structure before employing traditional algorithms. GNNs, an impressive framework for representation learning of graphs, have already produced many state-of-the-art techniques to solve node classification, link prediction, and graph classification tasks. GNNs can learn meaningful representations of graphs incorporating topological structure, node attributes, and neighborhood aggregation to solve supervised, semi-supervised, and unsupervised graph-based problems. In this study, the usefulness of GNNs has been analyzed primarily from two aspects - <strong>clustering and classification</strong>. We focus on these two techniques, as they are the most popular strategies in data mining to discern collected data and employ predictive analysis.</p> Biomechanical engineering Neural engineering Health promotion Preventative health care Applications in health Spatial data and applications Evolutionary computation Natural language processing Planning and decision making Data engineering and data science Data mining and knowledge discovery Graph, social and multimedia data Information retrieval and web search Knowledge and information management Context learning Deep learning Neural networks Semi- and unsupervised learning Data structures and algorithms Graph neural network Node classification Graph clustering Temporal graphs dynamic graphs NODE2VEC Graph Attention Mechanism Hunting BiLSTM model EHR data colorectal Cancer Cancers Cancer symptoms symptom Symptom cluster studies Coauthorship networks network analysis Word2vec Hierarchical Clustering method Dunn index semantic analysis text mining Natural Language Processing Tool UMLS identifiers umls Clinical Data Management

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TARGETABLE MULTI-DRUG NANOPARTICLES FOR TREATMENT OF GLIOBLASTOMA WITH NEUROIMAGING ASSESSMENT

EXPLORING GRAPH NEURAL NETWORKS FOR CLUSTERING AND CLASSIFICATION