Matching Items (77)

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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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  • 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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Microglia Motility in the Context of a PDGF Induced Glioblastoma

Description

Tumor associated microglia-and-macrophages (TAMS) may constitute up to 30% of the composition of glioblastoma. Through mechanisms not well understood, TAMS are thought to aid the progression and invasiveness of glioblastoma.

Tumor associated microglia-and-macrophages (TAMS) may constitute up to 30% of the composition of glioblastoma. Through mechanisms not well understood, TAMS are thought to aid the progression and invasiveness of glioblastoma. In an effort to investigate properties of TAMS in the context of glioblastoma, I utilized data from a PDGF-driven rat model of glioma that highly resembles human glioblastoma. Data was collected from time-lapse microscopy of slice cultures that differentially labels glioma cells and also microglia cells within and outside the tumor microenvironment. Here I show that microglia localize in the tumor and move with greater speed and migration than microglia outside the tumor environment. Following previous studies that show microglia can be characterized by certain movement distributions based on environmental influences, in this study, the majority of microglia movement was characterized by a power law distribution with a characteristic power law exponent lower than outside the tumor region. This indicates that microglia travel at greater distances within the tumor region than outside of it.

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  • 2013-12

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Estimating GL-261 cell growth: A murine model for Glioblastoma Multiforme

Description

Glioblastoma Multiforme (GBM) is an aggressive and deadly form of brain cancer with a median survival time of about a year with treatment. Due to the aggressive nature of these

Glioblastoma Multiforme (GBM) is an aggressive and deadly form of brain cancer with a median survival time of about a year with treatment. Due to the aggressive nature of these tumors and the tendency of gliomas to follow white matter tracks in the brain, each tumor mass has a unique growth pattern. Consequently it is difficult for neurosurgeons to anticipate where the tumor will spread in the brain, making treatment planning difficult. Archival patient data including MRI scans depicting the progress of tumors have been helpful in developing a model to predict Glioblastoma proliferation, but limited scans per patient make the tumor growth rate difficult to determine. Furthermore, patient treatment between scan points can significantly compound the challenge of accurately predicting the tumor growth. A partnership with Barrow Neurological Institute has allowed murine studies to be conducted in order to closely observe tumor growth and potentially improve the current model to more closely resemble intermittent stages of GBM growth without treatment effects.

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  • 2014-05

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Wada basins of attraction in diffeomorphic maps

Description

Dividing the plane in half leaves every border point of one region a border point of both regions. Can we divide up the plane into three or more regions such

Dividing the plane in half leaves every border point of one region a border point of both regions. Can we divide up the plane into three or more regions such that any point on the boundary of at least one region is on the border of all the regions? In fact, it is possible to design a dynamical system for which the basins of attractions have this Wada property. In certain circumstances, both the Hénon map, a simple system, and the forced damped pendulum, a physical model, produce Wada basins.

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Date Created
  • 2013-05

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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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Date Created
  • 2013-05

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Lagrangian Skeletons in Hurricane Katrina

Description

This thesis shows analyses of mixing and transport patterns associated with Hurricane Katrina as it hit the United States in August of 2005. Specifically, by applying atmospheric velocity information from

This thesis shows analyses of mixing and transport patterns associated with Hurricane Katrina as it hit the United States in August of 2005. Specifically, by applying atmospheric velocity information from the Weather Research and Forecasting System, finite-time Lyapunov exponents have been computed and the Lagrangian Coherent Structures have been identified. The chaotic dynamics of material transport induced by the hurricane are results from these structures within the flow. Boundaries of the coherent structures are highlighted by the FTLE field. Individual particle transport within the hurricane is affected by the location of these boundaries. In addition to idealized fluid particles, we also studied inertial particles which have finite size and inertia. Basing on established Maxey-Riley equations of the dynamics of particles of finite size, we obtain a reduced equation governing the position process. Using methods derived from computer graphics, we identify maximizers of the FTLE field. Following and applying these ideas, we analyze the dynamics of inertial particle transport within Hurricane Katrina, through comparison of trajectories of dierent sized particles and by pinpointing the location of the Lagrangian Coherent Structures.

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Date Created
  • 2012-12

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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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  • 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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Lagrangian Transport of Inertial Particles in Hurricane Katrina

Description

Using weather data from the Weather Research and Forecasting model (WRF), we analyze the transport of inertial particles in Hurricane Katrina in order to identify coherent patterns of motion. For

Using weather data from the Weather Research and Forecasting model (WRF), we analyze the transport of inertial particles in Hurricane Katrina in order to identify coherent patterns of motion. For our analysis, we choose a Lagrangian approach instead of an Eulerian approach because the Lagrangian approach is objective and frame-independent, and gives results which are better defined. In particular, we locate Lagrangian Coherent Structures (LCS), which are smooth sets of fluid particles which are locally most hyperbolic (either attracting or repelling). We implement a variational method for locating LCS and compare the results to previous computation of LCS using Finite-Time Lyapunov Exponents (FTLE) to identify regions of high stretching in the fluid flow.

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Date Created
  • 2013-05