Bayesian inference and its application to meta-analysis of epidemiologic data

In Part One, the foundations of Bayesian inference are reviewed, and the technicalities of the Bayesian method are illustrated. Part Two applies the Bayesian meta-analysis program, the Confidence Profile Method (CPM), to clinical trial data and evaluates the merits of using Bayesian meta-analysis for overviews of clinical trials.^ The Bayesian method of meta-analysis produced similar results to the classical results because of the large sample size, along with the input of a non-preferential prior probability distribution. These results were anticipated through explanations in Part One of the mechanics of the Bayesian approach. ^

The physician's perception of medical error and its application to the development of an educational training tool.

It is becoming clear that if we are to impact the rate of medical errors it will have to be done at the practicing physician level. The purpose of this project was to survey the attitude of physicians in Alabama concerning their perception of medical error, and to obtain their thoughts and desires for medical education in the area of medical errors. The information will be used in the development of a physician education program.

Breast cancer -specific activation of topoisomerase IIalpha and its application in the tumor -targeting gene therapy

To ensure the success of systemic gene therapy, it is critical to enhance the tumor specificity and activity of the promoter. In the current study, we identified the breast cancer-specific activity of the topoisomerase IIα promoter. We further showed that cdk2 and cyclin A activate topoisomerase IIα promoter in a breast cancer-specific manner. An element containing an inverted CCAAT box (ICB) was shown to respond this signaling. When the ICB-harboring topoisomerase IIα minimal promoter was linked with an enhancer sequence from the cytomegalovirus immediate early gene promoter (CMV promoter), this composite promoter, CT90, exhibited activity comparable to or higher than the CMV promoter in breast cancer cells in vitro and in vivo, yet expresses much lower activity in normal cell lines and normal organs than the CMV promoter. A CT90-driven construct expressing BikDD, a potent pro-apoptotic gene, was shown to selectively kill breast cancer cells in vitro and to suppress mammary tumor development in an animal model of intravenously administrated, liposome-delivered gene therapy. Expression of BikDD was readily detectable in the tumors but not in the normal organs of CT90-BikDD-treated animals. Finally, we demonstrated that CT90-BikDD treatment potentially enhanced the sensitivity of breast cancer cells to chemotherapeutic agents, especially doxorubicin and taxol. The results indicate that liposomal CT90-BikDD is a novel and effective systemic breast cancer-targeting gene therapy, and its combination with chemotherapy may further improve the current adjuvant therapy for breast cancer. ^

Gene therapy of cancer: Mechanisms of ``bystander effect'' killing in cells expressing the herpes simplex virus-thymidine kinase gene and its potential clinical applications

At the fore-front of cancer research, gene therapy offers the potential to either promote cell death or alter the behavior of tumor-cells. One example makes use of a toxic phenotype generated by the prodrug metabolizing gene, thymidine kinase (HSVtk) from the Herpes Simplex Virus. This gene confers selective toxicity to a relatively nontoxic prodrug, ganciclovir (GCV). Tumor cells transduced with the HSVtk gene are sensitive to 1-50 $\mu$M GCV; normal tissue is insensitive up to 150-250 $\mu$M GCV. Utilizing these different sensitivities, it is possible to selectively ablate tumor cells expressing this gene. Interestingly, if a HSVtk$\sp+$ expressing population is mixed with a HSVtk$\sp-$ population at high density, all the cells are killed after GCV administration. This phenomenon for killing all neighboring cells is termed the "bystander effect", which is well documented in HSVtk$\sp-$ GCV systems, though its exact mechanism of action is unclear.^ Using the mouse colon carcinoma cell line CT26, data are presented supporting possible mechanisms of "bystander effect" killing of neighboring CT26-tk$\sp-$cells. A major requirement for bystander killing is the prodrug GCV: as dead or dying CT26tk$\sp+$ cells have no toxic effect on neighboring cells in its absence. In vitro, it appears the bystander effect is due to transfer of toxic GCV-metabolites, through verapamil sensitive intracellular-junctions. Additionally, possible transfer of the HSVtk enzyme to bystander cells after GCV addition, may play a role in bystander killing. A nude mouse model suggests that in a 50/50 (tk$\sp+$/tk$\sp-$) mixture of CT26 cells the bystander eradication of tumors does not involve an immune component. Additionally in a possible clinical application, the "bystander effect" can be directly exploited to eradicate preexisting CT26 colon carcinomas in mice by intratumoral implantation of viable or lethally irradiated CT26tk$\sp+$ cells and subsequent GCV administration. Lastly, an application of this toxic phenotype gene to a clinical marking protocol utilizing a recombinant adenoviral vector carrying the bifunctional protein GAL-TEK to eradicate spontaneously-arisen or vaccine-induced fibrosarcomas in cats is demonstrated. ^

New algorithms for automated phylogenetic reconstruction using artificial intelligence and data mining techniques

Academic and industrial research in the late 90s have brought about an exponential explosion of DNA sequence data. Automated expert systems are being created to help biologists to extract patterns, trends and links from this ever-deepening ocean of information. Two such systems aimed on retrieving and subsequently utilizing phylogenetically relevant information have been developed in this dissertation, the major objective of which was to automate the often difficult and confusing phylogenetic reconstruction process. ^ Popular phylogenetic reconstruction methods, such as distance-based methods, attempt to find an optimal tree topology (that reflects the relationships among related sequences and their evolutionary history) by searching through the topology space. Various compromises between the fast (but incomplete) and exhaustive (but computationally prohibitive) search heuristics have been suggested. An intelligent compromise algorithm that relies on a flexible “beam” search principle from the Artificial Intelligence domain and uses the pre-computed local topology reliability information to adjust the beam search space continuously is described in the second chapter of this dissertation. ^ However, sometimes even a (virtually) complete distance-based method is inferior to the significantly more elaborate (and computationally expensive) maximum likelihood (ML) method. In fact, depending on the nature of the sequence data in question either method might prove to be superior. Therefore, it is difficult (even for an expert) to tell a priori which phylogenetic reconstruction method—distance-based, ML or maybe maximum parsimony (MP)—should be chosen for any particular data set. ^ A number of factors, often hidden, influence the performance of a method. For example, it is generally understood that for a phylogenetically “difficult” data set more sophisticated methods (e.g., ML) tend to be more effective and thus should be chosen. However, it is the interplay of many factors that one needs to consider in order to avoid choosing an inferior method (potentially a costly mistake, both in terms of computational expenses and in terms of reconstruction accuracy.) ^ Chapter III of this dissertation details a phylogenetic reconstruction expert system that selects a superior proper method automatically. It uses a classifier (a Decision Tree-inducing algorithm) to map a new data set to the proper phylogenetic reconstruction method. ^