Thesis (M. Nurs.)--University of Hong Kong, 2006.

Thesis (Ph.D.)--Kent State University, 2008. / Title from PDF t.p. (viewed Oct. 29, 2009). Advisor: Donna S. Martsolf. Keywords: Organizational culture; Clinical pathway; Ethnography; Thailand; Conscientiousness. Includes bibliographical references (p. 133-140).

published_or_final_version / Nursing Studies / Master / Master of Nursing in Advanced Practice

Accurate identification of small-molecule compound substrates and effectors, within specific tissues, represents a highly relevant yet equally elusive objective. Accomplishing this goal would have major implications on the assessment of compound efficacy and potential toxicity with significant impact on drug discovery and development. Computationally, there are no methods to elucidate a compound mechanisms of action (MoA) in cell-context-specific and genome-wide fashions. Experimental approaches are equally limited in that they are effective in identifying only specific drug substrate classes (e.g., high-affinity substrates of kinase inhibitors) rather than the full repertoire of proteins that effect compound activity in a specific tissue, including those that may cause undesired toxicity. They are costly, laborious, and the relevant mechanistic assays can only be performed in vitro. Here I introduce DeMAND, a novel algorithm for the regulatory network-based elucidation of compound Mechanisms of Action. The algorithm interrogates a context-specific regulatory network using at least six gene-expression profiles representative of in vitro or in vivo compound perturbation to identify compound dysregulated sub-networks as well as substrates and effector proteins. In experimental tests, the algorithm correctly identified proteins in the established MoA of over 90% of the tested compounds, including protein such as SIK1, a private effector of doxorubicin responsible for its cardiac toxicity, which is however not affected by less toxic topoisomerase inhibitors, such as camptothecin. Using gene expression profiles following perturbation of diffuse large B cell lymphoma cells with 14 and 92 compounds, respectively, at different concentrations and time points, I identified and validated several novel effector proteins. These include RPS3A (ribosomal protein S3A), VHL (von Hippel-Lindau tumor suppressor, E3 ubiquitin protein ligase), and CCNB1 (cyclin B1) as effectors of the mitotic spindle inhibitor vincristine, all of which significantly affected microtubule architecture and/or modulated vincristine activity when silenced, as well as JAK2 (Janus kinase 2) as a novel effector/modulator of mitomycin C, which desensitizes cells to mitomycin C treatment when silenced. Finally, I used DeMAND to evaluate compound similarity by comparing the proteins in their MoA. I tested the similarity of altretamine, a compound with currently unknown substrates, and sulfasalazine, which were predicted to have similar MoA and in particular to be inhibitors of the GPX4 (glutathione peroxidase 4) protein. Experimental validation confirmed this prediction as well as increase in lipid reactive oxygen species (ROS) levels, a recently established downstream effector of sulfasalazine. Critically, DeMAND suggests that regulatory networks reverse engineered de novo form large molecular profile datasets can provide novel mechanistic insight into drug activity, thus providing a significant novel contribution to our search for highly specific and non-toxic small-molecule inhibitors.

Source: Masters Abstracts International, Volume: 46-05, page: 2660. Adviser: Arch I. Carson. Includes bibliographical references.

Thesis (Ph. D. in Psychology)--Vanderbilt University, Dec. 2008. / Title from title screen. Includes bibliographical references.

Thesis (M.S.)--University of Michigan, 1989.

Thesis (M.S.)--University of Michigan, 1989.

The efforts addressed in this thesis refer to applying nonlinear risk predictive techniques based on logistic regression to medical diagnostic test data. This study is motivated and pursued to address the following: 1. To extend logistic regression model of biostatistics to medical informatics 2. Computational preemptive and predictive testing to determine the probability of occurrence (p) of an event by fitting a data set to a (logit function) logistic curve: Finding upper and lower bounds on p based on stochastical considerations 3. Using the model developed on available (clinical) data to illustrate the bounds-limited performance of the prediction. Relevant analytical methods, computational efforts and simulated results are presented. Using the results compiled, the risk evaluation in medical diagnostics is discussed with real-world examples. Conclusions are enumerated and inferences are made with directions for future studies. / by Alice Horn Dupont. / Thesis (M.S.C.S.)--Florida Atlantic University, 2011. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web.

This dissertation develops several new statistical methods for disease classification that directly account for the unique logic structure of criteria sets found in the Diagnostic and Statistical Manual of Mental Disorders. For psychiatric disorders, a clinically significant anatomical or physiological deviation cannot be used to determine disease status. Instead, clinicians rely on criteria sets from the Diagnostic and Statistical Manual of Mental Disorders to make diagnoses. Each criteria set is comprised of several symptom domains, with the domains determined by expert opinion or psychometric analyses. In order to be diagnosed, an individual must meet the minimum number of symptoms, or threshold, required for each domain. If both the overall number of domains and the number of symptoms within each domain are small, an exhaustive search to determine these thresholds is feasible, with the thresholds chosen to minimize the overall misclassification rate. However, for more complicated scenarios, such as incorporating a continuous biomarker into the diagnostic criteria, a novel technique is necessary. In this dissertation, we propose several novel approaches to empirically determine these thresholds. Within each domain, we start by fitting a linear discriminant function based upon a sample of individuals in which disease status and the number of symptoms present in that domain are both known. Since one must meet the criteria for all domains, an overall positive diagnosis is only issued if the prediction in each domain is positive. Therefore, the overall decision rule is the intersection of all the domain specific rules. We fit this model using several approaches. In the first approach, we directly apply the framework of the support vector machine (SVM). This results in a non-convex minimization problem, which we can approximate by an iterative algorithm based on the Difference of Convex functions algorithm. In the second approach, we recognize that the expected population loss function can be re-expressed in an alternative form. Based on this alternative form, we propose two more iterative algorithms, SVM Iterative and Logistic Iterative. Although the number of symptoms per domain for the current clinical application is small, the proposed iterative methods are general and flexible enough to be adapted to complicated settings such as using continuous biomarker data, high-dimensional data (for example, imaging markers or genetic markers), other logic structures, or non-linear discriminant functions to assist in disease diagnosis. Under varying simulation scenarios, the Exhaustive Search and both proposed methods, SVM Iterative and Logistic Iterative, have good performance characteristics when compared with the oracle decision rule. We also examine one simulation in which the Exhaustive Search is not feasible and find that SVM Iterative and Logistic Iterative perform quite well. Each of these methods is then applied to a real data set in order to construct a criteria set for Complicated Grief, a new psychiatric disorder of interest. As the domain structure is currently unknown, both a two domain and three domain structure is considered. For both domain structures, all three methods choose the same thresholds. The resulting criteria sets are then evaluated on an independent data set of cases and shown to have high sensitivities. Using this same data, we also evaluate the sensitivity of three previously published criteria sets for Complicated Grief. Two of the three published criteria sets show poor sensitivity, while the sensitivity of the third is quite good. To fully evaluate our proposed criteria sets, as well as the previously published sets, a sample of controls is necessary so that specificity can also be assessed. The collection of this data is currently ongoing. We conclude the dissertation by considering the influence of study design on criteria set development and its evaluation. We also discuss future extensions of this work such as handling complex logic structures and simultaneously discovering both the domain structure and domain thresholds.