Matching Items (144)
Description
Background:
Data assimilation refers to methods for updating the state vector (initial condition) of a complex spatiotemporal model (such as a numerical weather model) by combining new observations with one or more prior forecasts. We consider the potential feasibility of this approach for making short-term (60-day) forecasts of the growth and spread of a malignant brain cancer (glioblastoma multiforme) in individual patient cases, where the observations are synthetic magnetic resonance images of a hypothetical tumor.
Results:
We apply a modern state estimation algorithm (the Local Ensemble Transform Kalman Filter), previously developed for numerical weather prediction, to two different mathematical models of glioblastoma, taking into account likely errors in model parameters and measurement uncertainties in magnetic resonance imaging. The filter can accurately shadow the growth of a representative synthetic tumor for 360 days (six 60-day forecast/update cycles) in the presence of a moderate degree of systematic model error and measurement noise.
Conclusions:
The mathematical methodology described here may prove useful for other modeling efforts in biology and oncology. An accurate forecast system for glioblastoma may prove useful in clinical settings for treatment planning and patient counseling.
ContributorsKostelich, Eric (Author) / Kuang, Yang (Author) / McDaniel, Joshua (Author) / Moore, Nina Z. (Author) / Martirosyan, Nikolay L. (Author) / Preul, Mark C. (Author) / College of Liberal Arts and Sciences (Contributor)
Created2011-12-21
Description
Current culturing methods allow for human neural progenitor cells to be differentiated into neurons for use in diagnostic tools and disease modeling. An issue arises in the relatively low number of cells that can be successfully expanded and differentiated using these current methods, making the progress of research dependent on these cultures as a large number of cells are needed to conduct relevant assays. This project focuses on the expansion and differentiation of human neural progenitor cells cultured on microcarriers and within a rotating bioreactor system as a way to increase the total number of cells generated. Additionally, cryopreservation and the characteristics of these neurons post thaw is being investigated to create a way for long term storage, as well as, a method for standardizing cell lines between multiple experiments at different time points. The experiments covered in this study are aimed to compare the characteristics of differentiated human neurons, both demented and non-demented cell lines between pre-cryopreservation, freshly differentiated neurons and post-cryopreservation neurons. The assays conducted include immunofluorescence, calcium imaging, quantitative polymerase chain reaction, flow cytometry and ELISA data looking at Alzheimer’s disease traits. With the data collected within this study, the use of bioreactors, in addition to, cryopreservation of human neurons for long term storage can be better implemented into human neural progenitor cell research. Both of these aspects will increase the output of these cultures and potentially remove the bottleneck currently found within human neural disease modeling.
ContributorsHenson, Tanner Jay (Author) / Brafman, David (Thesis director) / Kodibagkar, Vikram (Committee member) / School of Life Sciences (Contributor) / Harrington Bioengineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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 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.
ContributorsManning, Miles (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Preul, Mark (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor)
Created2012-12
Description
Pharmacokinetic analysis is used in drug discovery programs to quantify the dynamics of an exogenous compound in a living organism. Compounds targeting the brain face additional challenges as the permeability of the blood brain barrier limits distribution of the compound into the brain tissue. Quantifying the permeability of the blood brain barrier is typically performed by euthanizing the animal at multiple time points in the study, requiring a large cohort of animals and prohibitively expensive amounts of the target compound. Previous studies have explored the use of in vivo fluorescent images as an alternative method to determine brain pharmacokinetics, but have faced challenges in quantifying the extravasation of the compound from the blood vasculature into the brain due to light scattering through the tissue prior to reaching the optical system. The correction model outlined in this study aims to correct for the effects of light scattering to enable more accurate quantification of extravasation and the dynamics of the system. The model utilizes the ratio of light scattering between the vascular and parenchymal regions of the brain to correct the extracted data. The model improved the quantification of extravasation on the positive control and provided a better understanding of the dynamics of the system, but failed to accurately quantify extravasation in the negative control and experimental analysis. Future study is needed to validate the model for the positive control and determine inclusion/exclusion criteria for experimental data.
ContributorsHack, William (Author) / Kodibagkar, Vikram (Thesis director) / Lifshitz, Jonathan (Committee member) / Griffiths, Daniel (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2024-05