Matching Items (75)

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Development of a Novel Smart Contrast Agent for Magnetic Resonance Imaging

Description

Smart contrast agents allow for noninvasive study of specific events or tissue conditions inside of a patient's body using Magnetic Resonance Imaging (MRI). This research aims to develop and characterize

Smart contrast agents allow for noninvasive study of specific events or tissue conditions inside of a patient's body using Magnetic Resonance Imaging (MRI). This research aims to develop and characterize novel smart contrast agents for MRI that respond to temperature changes in tissue microenvironments. Transmission Electron Microscopy, Nuclear Magnetic Resonance, and cell culture growth assays were used to characterize the physical, magnetic, and cytotoxic properties of candidate nanoprobes. The nanoprobes displayed thermosensitve MR properties with decreasing relaxivity with temperature. Future work will be focused on generating and characterizing photo-active analogues of the nanoprobes that could be used for both treatment of tissues and assessment of therapy.

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Date Created
  • 2014-05

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Modeling and Characterization of Mass Transfer Kinetics in Tumor Tissue Using Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI)

Description

The objective of the research presented here was to validate the use of kinetic models for the analysis of the dynamic behavior of a contrast agent in tumor tissue and

The objective of the research presented here was to validate the use of kinetic models for the analysis of the dynamic behavior of a contrast agent in tumor tissue and evaluate the utility of such models in determining kinetic properties - in particular perfusion and molecular binding uptake associated with tissue hypoxia - of the imaged tissue, from concentration data acquired with dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) procedure. Data from two separate DCE-MRI experiments, performed in the past, using a standard contrast agent and a hypoxia-binding agent respectively, were analyzed. The results of the analysis demonstrated that the models used may provide novel characterization of the tumor tissue properties. Future research will work to further characterize the physical significance of the estimated parameters, particularly to provide quantitative oxygenation data for the imaged tissue.

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Date Created
  • 2013-12

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Novel siloxane nanoprobes for molecular and cellular imagin

Description

Oxygen delivery is crucial for the development of healthy, functional tissue. Low tissue oxygenation, or hypoxia, is a characteristic that is common in many tumors. Hypoxia contributes to tumor malignancy

Oxygen delivery is crucial for the development of healthy, functional tissue. Low tissue oxygenation, or hypoxia, is a characteristic that is common in many tumors. Hypoxia contributes to tumor malignancy and can reduce the success of chemotherapy and radiation treatment. There is a current need to noninvasively measure tumor oxygenation or pO2 in patients to determine a personalized treatment method. This project focuses on creating and characterizing nanoemulsions using a pO2 reporter molecule hexamethyldisiloxane (HMDSO) and its longer chain variants as well as assessing their cytotoxicity. We also explored creating multi-modal (MRI/Fluorescence) nanoemulsions.

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Created

Date Created
  • 2013-05

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Modeling Brain Tumors: Simulating individual Patient Cases of Glioblastoma Multiforme

Description

Glioblastoma multiforme (GBMs) is the most prevalent brain tumor type and causes approximately 40% of all non-metastic primary tumors in adult patients [1]. GBMs are malignant, grade-4 brain tumors, the

Glioblastoma multiforme (GBMs) is the most prevalent brain tumor type and causes approximately 40% of all non-metastic primary tumors in adult patients [1]. GBMs are malignant, grade-4 brain tumors, the most aggressive classication as established by the World Health Organization and are marked by their low survival rate; the median survival time is only twelve months from initial diagnosis: Patients who live more than three years are considered long-term survivors [2]. GBMs are highly invasive and their diffusive growth pattern makes it impossible to remove the tumors by surgery alone [3]. The purpose of this paper is to use individual patient data to parameterize a model of GBMs that allows for data on tumor growth and development to be captured on a clinically relevant time scale. Such an endeavor is the rst step to a clinically applicable predictions of GBMs. Previous research has yielded models that adequately represent the development of GBMs, but they have not attempted to follow specic patient cases through the entire tumor process. Using the model utilized by Kostelich et al. [4], I will attempt to redress this deciency. In doing so, I will improve upon a family of models that can be used to approximate the time of development and/or structure evolution in GBMs. The eventual goal is to incorporate Magnetic Resonance Imaging (MRI) data into a parameterized model of GBMs in such a way that it can be used clinically to predict tumor growth and behavior. Furthermore, I hope to come to a denitive conclusion as to the accuracy of the Koteslich et al. model throughout the development of GBMs tumors.

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Created

Date Created
  • 2012-12

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Predicting Glioblastoma Growth Using a Poisson Process

Description

In this research we consider stochastic models of Glioblastoma Multiforme brain tumors. We first look at a model by K. Swanson et al., which describes the dynamics as random diffusion

In this research we consider stochastic models of Glioblastoma Multiforme brain tumors. We first look at a model by K. Swanson et al., which describes the dynamics as random diffusion plus deterministic logistic growth. We introduce a stochastic component in the logistic growth in the form of a random growth rate defined by a Poisson process. We show that this stochastic logistic growth model leads to a more accurate evaluation of the tumor growth compared its deterministic counterpart. We also discuss future plans to incorporate individual patient geometry, extend the model to three dimensions and to incorporate effects of different treatments into our model, in collaboration with a local hospital.

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Created

Date Created
  • 2013-12

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Stochastic parameterization of the proliferation-diffusion model of brain cancer in a Murine model

Description

Glioblastoma multiforme (GBM) is a malignant, aggressive and infiltrative cancer of the central nervous system with a median survival of 14.6 months with standard care. Diagnosis of GBM is made

Glioblastoma multiforme (GBM) is a malignant, aggressive and infiltrative cancer of the central nervous system with a median survival of 14.6 months with standard care. Diagnosis of GBM is made using medical imaging such as magnetic resonance imaging (MRI) or computed tomography (CT). Treatment is informed by medical images and includes chemotherapy, radiation therapy, and surgical removal if the tumor is surgically accessible. Treatment seldom results in a significant increase in longevity, partly due to the lack of precise information regarding tumor size and location. This lack of information arises from the physical limitations of MR and CT imaging coupled with the diffusive nature of glioblastoma tumors. GBM tumor cells can migrate far beyond the visible boundaries of the tumor and will result in a recurring tumor if not killed or removed. Since medical images are the only readily available information about the tumor, we aim to improve mathematical models of tumor growth to better estimate the missing information. Particularly, we investigate the effect of random variation in tumor cell behavior (anisotropy) using stochastic parameterizations of an established proliferation-diffusion model of tumor growth. To evaluate the performance of our mathematical model, we use MR images from an animal model consisting of Murine GL261 tumors implanted in immunocompetent mice, which provides consistency in tumor initiation and location, immune response, genetic variation, and treatment. Compared to non-stochastic simulations, stochastic simulations showed improved volume accuracy when proliferation variability was high, but diffusion variability was found to only marginally affect tumor volume estimates. Neither proliferation nor diffusion variability significantly affected the spatial distribution accuracy of the simulations. While certain cases of stochastic parameterizations improved volume accuracy, they failed to significantly improve simulation accuracy overall. Both the non-stochastic and stochastic simulations failed to achieve over 75% spatial distribution accuracy, suggesting that the underlying structure of the model fails to capture one or more biological processes that affect tumor growth. Two biological features that are candidates for further investigation are angiogenesis and anisotropy resulting from differences between white and gray matter. Time-dependent proliferation and diffusion terms could be introduced to model angiogenesis, and diffusion weighed imaging (DTI) could be used to differentiate between white and gray matter, which might allow for improved estimates brain anisotropy.

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Created

Date Created
  • 2016-05

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Direct nose-to-brain delivery of targeted polymeric nanoparticles

Description

There is growing interest in intranasal delivery of therapeutics because of direct nose-to-brain pathways which are able to bypass biological barriers, such as the blood-brain barrier (BBB), that have historically

There is growing interest in intranasal delivery of therapeutics because of direct nose-to-brain pathways which are able to bypass biological barriers, such as the blood-brain barrier (BBB), that have historically limited our ability to effectively deliver drugs to the central nervous system (CNS). Since these pathways were first discovered, there has been significant preclinical success in delivering a wide range of therapeutics to the CNS with additional growing efforts to further improve delivery through nanoparticle drug delivery systems. Here we sought to improve intranasal delivery of DiR, a lipophilic small molecule cyanine dye, to the CNS by surface modifying a poly (lactic-co-glycolic acid) (PLGA) nanoparticle with a short peptide derived from the rabies virus glycoprotein (RVG). The specific aims of this thesis were to evaluate administration route-dependent delivery of RVG nanoparticles to the CNS, and to identify anatomical transport pathways by which nanoparticles facilitate transport of small lipophilic molecules. Route-dependent delivery kinetics and distribution were studied by administering DiR loaded nanoparticles to healthy Balb/C mice. Specific tissues were homogenized and the fluorescent intensity of DiR was measured and compared to control tissue spiked with known amounts of dye. While bioavailability of DiR after intranasal administration was near 0% with minimal exposure to peripheral organs, quick and efficient delivery to the CNS was still observed. CNS delivery after intranasal administration was rapid with peak concentrations at 30 minutes post-administration followed by broad clearance by 2 hours. Regional differences of delivery of DiR to the CNS demonstrated engagement of direct nose-to-brain transport pathways with high delivery being observed to the olfactory bulb, brain stem, and trigeminal nerve. RVG modification however presented only modest targeting benefits. In conclusion, the biodistribution of DiR after intranasal administration of DiR loaded nanoparticles showed high potential for the direct nose-to-brain delivery while limiting peripheral exposure of lipophilic small molecule drugs.

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Created

Date Created
  • 2016-05

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Improving the Realism and Magnetic Resonance Imaging of Multicellular Tumor Spheroids

Description

Magnetic resonance imaging (MRI) of changes in metabolic activity in tumors and metabolic abnormalities can provide a window to understanding the complex behavior of malignant tumors. Both diagnostics and treatment

Magnetic resonance imaging (MRI) of changes in metabolic activity in tumors and metabolic abnormalities can provide a window to understanding the complex behavior of malignant tumors. Both diagnostics and treatment options can be improved through the further comprehension of the processes that contribute to tumor malignancy and growth. By detecting and disturbing this activity through personalized treatments, it is the hope to provide better diagnostics and care to patients. Experimenting with multicellular tumor spheroids (MCTS) allows for a rapid, inexpensive and convenient solution to studying multiple in vitro tumors. High quality magnetic resonance images of small samples, such as spheroid, however, are difficult to achieve with current radio frequency coils. In addition, in order for the information provided by these scans to accurately represent the interactions and metabolic activity in vivo, there is a need for a perfused vascular network. A perfused vascular network has the potential to improve metabolic realism and particle transport within a tumor spheroid. By creating a more life-like cancer model and allowing the progressive imaging of metabolic functions of such small samples, a better, more efficient mode of studying metabolic activity in cancer can be created and research efforts can expand. The progress described in this paper attempts to address both of these current shortcomings of metabolic cancer research and offers potential solutions, while acknowledging the potential of future work to improve cancer research with MCTS.

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Created

Date Created
  • 2016-12

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Naïve Bayes Classification for Analyzing Prostate Cancer Treatment Outcomes

Description

Prostate cancer is the second most common kind of cancer in men. Fortunately, it has a 99% survival rate. To achieve such a survival rate, a variety of aggressive therapies

Prostate cancer is the second most common kind of cancer in men. Fortunately, it has a 99% survival rate. To achieve such a survival rate, a variety of aggressive therapies are used to treat prostate cancers that are caught early. Androgen deprivation therapy (ADT) is a therapy that is given in cycles to patients. This study attempted to analyze what factors in a group of 79 patients caused them to stick with or discontinue the treatment. This was done using naïve Bayes classification, a machine-learning algorithm. The usage of this algorithm identified high testosterone as an indicator of a patient persevering with the treatment, but failed to produce statistically significant high rates of prediction.

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Date Created
  • 2016-12

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A Novel Temperature-Sensitive MR & Fluorescence Imaging Contrast Agent

Description

Contrast agents in medical imaging can help visualize structural details, distributions of particular cell types, or local environment characteristics. Multi-modal imaging techniques have become increasingly popular for their improved sensitivity,

Contrast agents in medical imaging can help visualize structural details, distributions of particular cell types, or local environment characteristics. Multi-modal imaging techniques have become increasingly popular for their improved sensitivity, resolution, and ability to correlate structural and functional information. This study addresses the development of dual-modality (magnetic resonance/fluorescence) and dual-functional (thermometry/detection) nanoprobes for enhanced tissue imaging.

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Created

Date Created
  • 2015-05