Chronic disease care is a significant and growing problem in healthcare today. Current healthcare processes are more focused on dealing with acute episodes of care rather than the longitudinal care requirements of chronic disease. The use of information technology is one tool that may assist in improving chronic disease care. The goals of the study were to study workflow and information flow in three ambulatory chronic disease clinics to develop general models of workflow and information flow in chronic disease care. Over 150 hours of direct observation in the three clinics identified elements of the workflow and information flow, the features of existing informatics tools used, and gaps between user needs and existing functionality. Clinic-specific models of workflow, information flow, and temporal flow were developed. Semi-structured interviews were conducted to gather additional data and verify the models. Generalized models were developed that identified the common aspects of workflow and information flow across all three clinics. Aspects of chronic disease care workflow that are important to address in the design of informatics tools were identified. The study confirmed that there are core similarities between different chronic disease domains, although there are some crucial differences and suggested approaches to dealing with the unique needs of the chronic disease care environment.

Most functional MRI studies rely on fast Echo Planar Imaging, which is sensitive to magnetic susceptibility effects. These effects can lead to image-to-image signal instability, reducing the reliability of functional activation maps. Respiration is responsible for a significant component of image-to-image variance, but no widely effective method for correcting respiration-based susceptibility effects is available. We demonstrate a relationship between respiration-related susceptibility effects in the presence of parallel imaging acquisitions and apply our findings to analyze the behavior of IMPACT (IMage-based Physiological Artifact Correction Technique). Based on our findings regarding IMPACT, we propose COMPACT (Center of Mass-based Physiological Artifact Correction Technique), a new method which provides a reliable estimate of respiratory effects based on the motion of an image sets center of mass (centroid) through time.

Diffusion Tensor Imaging (DTI) has become the primary imaging modality for non-invasive characterization of the microstructure of living tissues, particularly of human white matter. Despite its success in various research areas and clinical applications, DTI is unable to describe adequately non-Gaussian diffusion. Fiber ORientation Estimated using Continuous Axially Symmetric Tensors (FORECAST), a new approach to High Angular Resolution Diffusion (HARD) analysis, is able to provide reliable estimates of the fiber radial diffusivity and orientation distribution within each voxel. In this study, several techniques were developed to enhance the FORECAST models reproducibility. The models dependence on various imaging parameters and analysis parameters was tested by Monte Carlo simulation. The optimal parameters for FORECAST analysis was determined based on the simulation results, and verified by in vivo human data.

In hepatic applications, radiofrequency ablation (RFA) produces ablation extents that are limited in size both as a result of local tissue properties as well as constraints in ablation device design and physics. Because RFA is a focal, nonconformal therapeutic modality, proper placement of the device is an important goal in producing successful treatment so that the resulting ablation extents overlap the detectable tumor as well as a suitably defined margin. This dissertation examines novel methods of treatment planning by using image-guided techniques to improve placement accuracy and computational modeling to predict ablation outcomes given suitable placements. A method is presented to search for needle placement that best satisfies a given therapeutic goal using outcomes predicted by finite element models of ablations. This search technique is applied to simulated scenarios requiring single as well as multiple ablations to study effects of nearby heat sinks on optimal placement. A phantom system is then constructed to conduct ablation experiments performed using a tracked RFA device. The phantom ablation results are compared against ablation extents predicted using computational models given the measured positional data from the tracked device. Metrics to quantify the model accuracy are introduced, and the effects of tracking inaccuracies are analyzed. Finally, the sensitivity of predicted ablations to needle placement inaccuracies is studied theoretically. Sensitivity analysis is conducted via a novel method that couples boundary element and finite element methods to obtain multiple simulations efficiently for different needle placements over a static mesh. This method is used with Monte Carlo simulations to generate a spatial map of the likelihood of ablation success given uncertainties in targeting accuracy. Using this technique, strategies to make treatment plans less sensitive to placement errors are studied. The results of this research demonstrate the feasibility of coupling image-guided techniques and computational modeling to produce predictive treatments plans for RFA that are robust to device placement uncertainties.

The collection of visual evidence of M13 bacteriophage displaying a nuclear localization heptapeptide localizing to the nuclei of breast cancer cells is described in this thesis. The bacteriophages displaying the heptapeptide were conjugated to a fluorochrome prior to incubation with live cells. After the incubation, the cells were fixed and cell nuclei were stained, and a fluorescent microscope was used to visualize and obtain photographs of the cells. Fluorochrome-labeled bacteriophages not displaying the heptapeptide showed no localization to cell nuclei, but fluorochrome-labeled bacteriophages displaying the heptapeptide exhibited peripheral nuclear binding. The second portion of this thesis describes an attempt to measure the amount of the metalloproteinase matrilysin produced by five cancer cell lines by analyzing supernantants taken at regular time points over a 96-hour interval. An ELISA using antibodies specific to matrilysin was performed initially, followed by an assay based on the cleavage activity of the matrilysin enzyme. The ELISA results indicated that the cell lines produced an undetectable level of matrilysin, while the activity assay indicated elevated production by all five of the cell lines. Diagnostic experiments were conducted to determine the cause of the substrate cleavage. It was determined that enzymes present in the samples other than metalloproteinases were cleaving the substrate. Efforts to isolate Cathepsin D, an aspartic protease which was not inhibited during the assays, detected its production by three of the five cell lines.

Recent advances in single nucleotide polymorphism (SNP) genotyping techniques, public databases, and genomic knowledge via the Human Genome Project and the Haplotype Mapping project (HapMap) allow for true genome-wide association (GWA) analysis for common complex diseases such as heart disease, diabetes, and Alzheimers. A major obstacle in genome-wide association analysis is the prohibitively high cost of projects that require genotyping hundreds, even thousands, of individuals in order to achieve appropriate statistical significance. One potential solution to the prohibitive cost is to combine or pool the DNA of case and control individuals and to use pooled genotyping or allelotyping for association analysis by determining the genotype allele frequency differences between case and control populations. While pooling can dramatically increase efficiencies by lowering cost and time, it also introduces additional sources of error and noise. In this study, we comparatively examine DNA pooled genotyping versus individual genotyping for genome-wide association analysis of complex disease. Our work has created a system and process that allows for the direct evaluation and comparison of pooled genotyping versus individual genotyping by using and modifying existing bioinformatics tools. Our results show that pooled GWA studies are limited to resolving complex disease with medium to high relative risks ratios. Pooling errors have a very large effect on the overall statistical significance of a pooled GWA. Genotyping errors have a modest effect on pooled and individual GWA which is much less in magnitude to pooling errors.

Mass Spectrometry (MS) is emerging as a breakthrough mass-throughput technology believed to have powerful potential for producing clinical diagnostic and prognostic models and for identifying relevant disease biomarkers. A major barrier to making mass spectrometry clinically useful and to exploring its potential in an efficient and reliable manner is the challenge posed by data analysis of proteomic spectra in order to produce reliable predictor models of disease and clinical outcomes. This thesis describes the development and evaluation of a fully-automated software system (FAST-AIMS), capable of analyzing mass spectra to produce high-quality diagnostic and outcome prediction models.

T cells occupy a central role in cell-mediated immunity and as such, deciphering the signaling events that govern T cell activity is critical in fully understanding the adaptive immune response. Current immunologic methodologies utilize either conventional cell culture techniques to analyze millions of cells over time, thereby averaging out rare signaling events, or technology that interrogates single cells at a single time point each, resulting in a loss of information regarding temporal signaling dynamics. To overcome these limitations, we have developed the multi-trap nanophysiometer, a novel, self-contained microfluidic platform fabricated of optically transparent, bio-inert PDMS designed to study signaling dynamics of multiple single T cells in parallel. This body of work describes the major accomplishments attained towards the development and validation of this platform. Cell viability analysis revealed that at flow rates of 100 nl/min, more than 70% of CD4+ T cells, held in place using only hydrodynamic forces, remained viable following 24 hours within the microfluidic environment. We then observed cytosolic calcium transients to demonstrate the ability to activate T cells within the multi-trap nanophysiometer using chemical, antibody, and cellular forms of stimulation. Applying this platform to study intercellular signaling events we were able to observe calcium transients in T cells in response to both contact- and non-contact-based interactions with dendritic cells. Further investigation revealed that in the absence of antigen, LPS-matured dendritic cells secrete chemical signals that induce calcium transients in naïve CD4+ T cells, but in such small concentrations that effects of these signals are not easily observed in normal cell culture conditions. Finally, utilizing the multi-trap nanophysiometer to study the immunological synapse between dendritic cells and T cells, we revealed the occurrence of bi-directional cytosolic dye transfer. This suggests that communication between dendritic cells and T cells during the immunological synapse may not be limited to cell surface interactions. Taken together, these results establish the multi-trap nanophysiometer as a powerful tool in the analysis of cell signaling dynamics.

The development of ultra-high field systems has benefited magnetic resonance imaging experiments due to the signal enhancement such systems provide. However, improvement in signal strength comes at the cost of increased artifacts caused by T2*, ´B0, and susceptability effects leading to image intensity loss, blurring, and geometrical distortion. As these effects are time and field dependent, techniques have been developed to speed up image acquisition. In particular, parallel imaging based methods have been developed that use the signal reception properties of a surface coil parallel array. Specifically, each coil has a distinct spatial sensitivity to the established image object intensity that provides additional acquired signal encoding. Techniques such as SENSE use knowledge of the coil sensitivities to remove aliasing artifacts that result when the image spatial frequencies are sparsely acquired. The reduction in data acquisition translates directly to a reduced scan time to diminish time-dependent artifacts or improve resolution, but at a loss in SNR due to data reduction and SENSE reconstruction errors. To date, parallel imaging approaches have been largely applied to human studies, with limited animal experiment application where they would also be of benefit. This thesis describes the development of a four channel parallel array and SENSE reconstruction program that enables parallel imaging studies to be performed on a Varian 9.4T small animal MR system.

Physicians use of a computerized inpatient sign-out tool has been shown to reduce the risk of preventable adverse events. The researcher evaluated sign-out software usage at Vanderbilt University Medical Center in order to understand user behavior and identify software enhancements. To accomplish these goals, the researcher created software to record sign-out data, determined descriptive statistics of software utilization, collected feedback from users regarding new software enhancements, and analyzed the content produced by sign-out tool users. Results included the identification of unanticipated software usage by non-providers, different use patterns across hospital units/services, and a variety of discipline-specific sign-out note styles. These results combined with a comparison to the literatures recommendations guided the design specification for new sign-out software. Further study is required to determine relevant outcome measures related to both the sign-out process and the impact of sign-out software.