<p> The motivation for this work is a vision of widespread adoption of a priori quantitative epidemiological information for clinical decision-making, and can be seen as a quantitative large-scale extension of evidence-based medicine (EBM). Medical images can be seen as a spatially encoded map of physiological measurements that can be used to predict prognosis and to drive treatment plans. This paradigm can be very powerful and is driven by the recent big data revolution in computer science as well as the increasing availability of medical imaging modalities due to decreases in manufacturing costs. In order to achieve this overarching goal, three practical requirements must be reached and correspond to the parts of this thesis: Part A: Developing IT infrastructure and technology that enables the dataset to be properly collected and organized for analysis. Part B &amp; C: Generation of functional (Part B) and structural (Part C) medical imaging contrast that are optimized for analysis. Part D: Pattern recognition techniques (including both image processing and machine learning techniques) to mine information from the large imaging datasets generated. As part of the thesis, I discuss my contribution to IT infrastructure (Part A) by developing a Short Message Service (SMS)-based system to control the clinically used Picture Archival and Communication System (PACS) (Ch.2) as well as an imaging study tool that categorizes patient imaging data for use in retrospective studies(Ch.3). I then go on to detail my work with functional neuroimaging of obesity using functional magnetic resonance imaging (fMRI)(Ch.4) and (Ch.5). Chapters 6-9 details my efforts at studying abnormal aging versus normal aging using diffusion MRI as well as applications of diffusion MRI to surgical planning. Chapters 10 discusses my work integrating diffusion MR with FLAIR MRI to investigate the properties of white matter lesions and how it can be used in the clinical setting. Chapter 11 then moves on to talk about my work modifying standard brain parcellation techniques to allow them to work with aged brains with large infarcts. Chapters 6-11 altogether represent my efforts in structural neuroimaging using MRI (Part C). The thesis then closes with capstone work in development staging using hand x-rays using fuzzy logic (Ch. 12 &amp; 13). To close the work with Alzheimer's Disease (AD) and aging, we used machine learning techniques to predict disease progression based on a baseline MRI scan as well as higher order analysis of our diffusion MRI dataset by integrating MRI information with other clinical information such as neuropsychological tests, cardiovascular status. This is all in an effort to computationally explore the relationship between MRI measurements and clinical presentation of disease as measured by neuropsychological scores. Similarly with the Obesity work, we related fMRI activation differences between high and low calorie foods with non-imaging information such as insulin resistance (Ch. 16).</p>

<p> We develop a multispecies continuum model to simulate the spatiotemporal dynamics of cell lineages in solid tumors is discussed. The model accounts for protein signaling factors produced by cells in lineages, and nutrients supplied by the microenvironment. We find that the combination therapy involving differentiation promoters and radiotherapy is very effective in eradicating such a tumor. We investigate the effect of production of various feedback factors by healthy tissue on tumor morphologies. Our simulation results show that the larger production rate of the negative feedback factor by healthy tissue surrounding the tumor, in general lead to smaller, more compact and more circular tumor shapes. However, the increase in the concentration of these feedback factors may have non-monotone effect on the tumor morphologies. We investigate the effect of initial shape on therapy effectiveness. The results from the simulations show that the initial tumor geometry might play an important role in tumor prognostic and the effectiveness of a specific treatment. We observe that the therapy is more effective on tumors that still respond to the signals received from the healthy tissue in comparison with the ones that do not respond to signaling factors (in this case differentiation signals) by stromal tissue or healthy tissue surrounding the tumor. It is shown that the tumors with larger shape factors and smaller areas (more elongated and thinner) respond better to treatment, and the combination therapy is more successful on tumors with such characteristics. We applied mathematical modeling of radiotherapy using experimental data provided from our collaborative work with radiational oncology department of University of California, Los Angeles. Our investigations show that in order to match the experimental results with the simulations, the dedifferentiation rate of non-stem cells should be increased as a function of radiation dose. It is also observed that the population of induced stem cells followed such exponential relationship with respect to therapy dose. The results from simulations and the analysis of the equations suggest that in order for the simulation results to match with the experimental data, the original stem cells and the induced stem cells may undergo direct differentiation.</p>

<p> Cancer is an ideal target for personal genomics-based medicine that uses high-throughput genome assays such as DNA sequencing, RNA sequencing, and expression analysis (collectively called <i>omics</i>); however, researchers and physicians are overwhelmed by the quantities of big data from these assays and cannot interpret this information accurately without specialized tools. To address this problem, I have created software methods and tools called <i>OCCAM</i> (OmiC&nbsp;data Cancer Analytic Model) and DIPSC (Differential Pathway Signature Correlation) for automatically extracting knowledge from this data and turning it into an actionable knowledge base called the <i>activitome.</i> An activitome signature measures a mutation's effect on the cellular molecular pathway. As well, activitome signatures can also be computed for clinical phenotypes. By comparing the vectors of activitome signatures of different mutations and clinical outcomes, intrinsic relationships between these events may be uncovered. OCCAM identifies activitome signatures that can be used to guide the development and application of therapies. DIPSC overcomes the confounding problem of correlating multiple activitome signatures from the same set of samples. In addition, to support the collection of this big data, I have developed <i>MedBook,</i> a federated distributed social network designed for a medical research and decision support system. OCCAM and DIPSC are two of the many apps that will operate inside of MedBook. MedBook extends the Galaxy system with a signature database, an end-user oriented application platform, a rich data medical knowledge-publishing model, and the Biomedical Evidence Graph (BMEG). The goal of MedBook is to improve the outcomes by learning from every patient.</p>

No description available.

<p> Paracrine signals, essential for the proper survival and functioning of tissues, can be mimicked by delivery of therapeutic proteins within engineered tissue constructs. Conventional delivery methods are of limited duration and are unresponsive to the local environment. I developed a system for sustained and regulated delivery of paracrine signals by encapsulating living cells of one type in alginate beads and co-suspending these cell-loaded particles along with unencapsulated cells of a second type within a 3D protein gel. By using cells as particulate protein delivery sources in a 3D gel, an array of soluble factors are delivered in an adaptable manner throughout the gel material for as long as the cells are alive and healthy.</p><p> This system was applied to vascular tissue engineering by placing human placental microvascular pericytes (PCs) in the particulate alginate phase and human umbilical vein endothelial cells (HUVECs) in the protein gel phase. Particle characteristics were optimized to keep the encapsulated PCs viable for at least two weeks. Encapsulated PCs were bioactive <i>in vitro</i>, secreting multiple angiogenic proteins for up to 7 days, including hepatocyte growth factor and vascular endothelial growth factor, and responding to externally applied HUVEC-derived signals. Medium conditioned by encapsulated PCs stimulated the formation of longer cumulative sprout lengths (1.4x) in an HUVEC sprouting assay.</p><p> Encapsulated PCs were biologically active in the complete dual-cell system described. These encapsulated PCs influenced HUVEC behavior in the surrounding gel by enhancing the formation of vessel-like structures, when compared to empty alginate bead controls. Significantly more multi-cell cords (3.1x) and tubes (2.6x) were formed by HUVEC in the presence of encapsulated PC. Additionally, multi-cell cords were significantly longer (1.4x) and lumen diameter was significantly smaller (1.5x) than in control gels containing empty alginate particles.</p><p> Encapsulated PCs appear to lead to important functional consequences in the developing vascular network: paracrine signals from the entrapped PCs lead to smaller lumen diameters in vessels formed by HUVEC that were suspended in protein gels and subcutaneously implanted in the abdominal wall of immunodeficient mice. In native <i>in vivo</i> microvessels, PCs reside in the basement membrane and directly contact EC tubes. Previous studies, in which EC-PC contact was permitted, showed that the presence of PCs lead to smaller vessel diameter. These earlier studies hypothesize that this limitation of lumen diameter requires EC-PC contact, and was due to a physical restriction. Our studies, on the other hand, suggest that paracrine signals contribute to this control of vessel diameter.</p><p> I conclude that alginate-encapsulated cells can provide functional paracrine signals within engineered tissues. The dual-cell system described in this dissertation can be used both as an engineering therapy to adaptably deliver an array of paracrine signals and as a platform for studying the purely paracrine interactions between two cell types in the absence of cell-cell contact.</p>

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008. / Source: Dissertation Abstracts International, Volume: 69-05, Section: B, page: 3217. Advisers: Kyekyoon Kim; Phillip H. Geil. Includes bibliographical references (leaves 85-93) Available on microfilm from Pro Quest Information and Learning.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006. / Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 3940. Adviser: Carolyn Beck. Includes bibliographical references (leaves 100-114) Available on microfilm from Pro Quest Information and Learning.

Thesis (Ph. D.)--University of Illinois at Urbana-Champaign, 2006. / Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 3807. Adviser: John A. Katzenellenbogen. Includes bibliographical references. Available on microfilm from Pro Quest Information and Learning.

Introduction: Around 80% of the population in sub-Saharan Africa use indigenous medicine when ill, before consulting biomedical health practitioners, and many only consult doctors when their disease is at an advanced stage or the indigenous medicine has failed. The rise in TB infection has led the South African government to consider collaboration between African indigenous health and biomedical health practitioners in order to counter a pandemic among communities. The World Health Organisation (WHO) advocates incorporating African indigenous health and biomedical health practitioners to promote quality service delivery among the communities. Support for cooperation between these health practitioners was promoted in South Africa with the passing of the Traditional Health Practitioners Act of 2007 (Act. 22 of 2007) to promote collaboration. The government introduced programmes and models of health promotion related to TB management but they focussed on the prevention of the disease, promotion of health, and programmes about HIV/AIDS rather than on how a model of convergence and engagement between the African indigenous health and biomedical health practitioners regarding treatment of TB should be created. Research design and methods: A qualitative, exploratory, descriptive and contextual research design was used. The study conducted within the biomedical health and African indigenous health practitioner's context and was conducted in three phases. Non-probability, purposive sampling was used to choose a population that comprised three groups of participants, namely, biomedical health practitioners, African indigenous health practitioners and supporters of direct observed treatment (DOT).The sample size was large enough to determine differences between groups. The researcher conducted in-depth individual interviews and focus group to explore and describe their views regarding convergence and engagement about the treatment of TB. Questions were asked following the interview guide until data saturation occur. Data analysis was conducted following Tesch method. Findings: The study found they perceived the treatment of TB differently, largely because of cultural diversity, for instance, differing beliefs and worldviews that had an impact on understanding the meaning of concepts such as disease, illness and wellness. Some believed that it was caused by witchcraft, others the result of cleansing rituals not having been carried out. It was also believed that Isidliso entered the victim while sleeping, in the form of food. Thus, the development of the model of convergence and engagement between the biomedical health and African indigenous health practitioners will sort out the myths that is in the mind of the people regarding the cause of TB. Conclusion: The model of convergence and engagement between the biomedical health and African indigenous health practitioners regarding the tuberculosis (TB) treatment was developed following the steps proposed by Dickoff, James and Wiedenbach and Walker and Avant. / Thesis (PhD)--University of Pretoria, 2016. / Nursing Science / PhD / Unrestricted

<p>Yttrium-90 (⁹⁰Y) is one of the most commonly used radionuclides in targeted radionuclide therapy (TRT). In treatment planning, reliable prediction of the 90Y distribution <i>in vivo</i> is essential to performing both safe and effective therapy. However, the distribution of surrogate agents used in treatment planning may not exactly predict the distribution of ⁹⁰Y. Thus it would be useful to image the ⁹⁰Y distribution after therapeutic administration to provide the ground truth for the ⁹⁰Y distribution. This would facilitate evaluating and potentially improving pre-therapy methods for individualizing and optimizing the therapy. Single photon emission computed tomography (SPECT) is a powerful imaging technique for estimating 3D distribution of radionuclides <i> in vivo.</i> However, as an essentially pure &beta;-particle emitter, ⁹⁰Y does not emit gamma photons considered appropriate for SPECT imaging. One possible solution is to image bremsstrahlung photons generated by the interaction of the 13-particles with atomic nuclei in the body. The continuous and broad energy distribution of bremsstrahlung photons, however, imposes substantial challenges on quantitative SPECT imaging. The overall goal of this work was to develop and evaluate new quantitative bremsstrahlung SPECT methods for improving the reliability (accuracy and precision) of the ⁹⁰Y activity estimates for the dosimetry application. </p><p> Reconstruction method, acquisition energy window, and collimator are three crucial factors that determine the reliability of quantitative SPECT imaging. </p><p> In this work, we first developed an improved quantitative reconstruction method. The improvement resulted from more accurate modeling of the image formation process in a statistical iterative reconstruction method. Improvements in the model included enhancements to the Monte Carlo (MC) bremsstrahlung simulation used to generate various components of the model and better modeling of the energy dependence of various image degrading effects through the use of multiple energy ranges. The evaluation, using both a physical phantom experiment and an XCAT phantom simulation, demonstrated more accurate modeling of the image formation process and more accurate organ activity estimates than previous methods. </p><p> We then developed new methods for optimizing the acquisition energy window and parallel-hole collimator, respectively, for quantitative imaging. These methods account for the effects of energy window or collimator on both the bias and the variance of the activity estimates, and are applicable to radionuclides with any type of emission energy spectra. We applied these methods to optimizing the energy window and collimator for quantitative ⁹⁰Y bremsstrahlung SPECT in microsphere brachytherapy. </p><p> In addition to improving the reliability of quantitative imaging, we also did some work on improving the visual image quality for ⁹⁰Y bremsstrahlung SPECT imaging. We optimized the energy window for a detection task based on the performance of an observer that accounts for the degradation of the image quality due to model-mismatch. This is important as detection of post-administration extra-hepatic 90Y could be useful in predicting and preparing for complications such as radiation-induced gastro-intestinal ulcerations. </p>