Introduction The long-term patency of the grafts used during the coronary artery bypass grafting (CABG) is one of the most significant predictors of the clinical outcomes. The gold standard graft used during CABG with the best long-term patency rate and the better clinical outcomes is left internal thoracic artery (LITA) grafted to the left coronary artery (LCA). The controversy lies in choosing the second-best conduit for the non-left coronary artery (NLCA) with similar patency rate as LITA. This meta-analysis examines the long-term patency and clinical outcomes of all arterial grafts versus all venous grafts used during the CABG. Methods A comprehensive literature search of all published randomized control trials (RCTs) assessing long-term patency and clinical outcomes of grafts used in CABG was conducted using PubMed, Cochrane Central Registry of Controlled Trials, and Google Scholar (1966-2018). Keywords searched included combinations of "CABG", "venous grafts in CABG", "arterial grafts in CABG", "radial artery grafts in CABG", "gastroepiploic artery grafts in CABG", "patency and clinical outcomes". Inclusion criteria included: RCTs comparing the long-term patency, and clinical outcomes of radial artery, right internal thoracic artery, gastroduodenal artery, and saphenous vein grafts used in CABG. Long-term patency of the grafts and clinical outcomes were analyzed. Results Eight RCTs involving 2,091 patients with 1,164 patients receiving arterial grafts and 927 patients receiving venous grafts were included. There was no difference between the long-term patency rate (relative risk (RR) = 1.050, 95% confidence interval (CI) = 0.949 to 1.162, and p = 0.344), overall mortality rate (RR = 1.095, 95% CI = 0.561 to 2.136, and p = 0.790), rate of myocardial infarction (MI) (RR = 0.860, 95% CI = 0.409 to 1.812, and P = 0.692), and re-intervention rate (RR = 0.0768, 95% CI = 0.419 to 1.406, and P = 0.392) between arterial and venous grafts. Conclusion The use of arterial conduits over the venous conduits has no significant superiority regarding the long-term graft patency, the rate of MI, overall mortality, and the rate of revascularization following CABG. Additional adequately powered studies are needed to further evaluate the long-term outcomes of arterial and venous grafts following the CABG.

Introduction: Diabetes Mellitus is a metabolic disorder that affects a person’s ability to either produce insulin (Type I diabetes mellitus) or properly use insulin (Type II diabetes mellitus) in order to maintain adequate blood glucose levels. The most severe diabetic complications arise due to hyperglycemia – a state of extremely high blood glucose levels – such as, coronary artery disease (CAD), in which coronary stent therapy is a popular method of treatment. However, research has shown a high rate of in-stent restenosis in diabetic patients with CAD, most likely due to activation of cellular adhesion molecules on endothelial cells exposed to the hyperglycemic environment. Blood vessel mimics (BVMs) have been researched as viable options for in vitro studies on vascular stents; thus, it would be beneficial to create an in vitro diabetic BVM for stent manufactures to evaluate and determine the root cause of the high failure rate of stents in the diabetic population. In addition, a diabetic BVM would help manufactures optimize coatings or stent configurations for diabetic patients. Methods: The purpose of this thesis was to take the initial steps towards the goal of a diabetic BVM. The first aim was to establish a procedure of developing glycemic cell media solutions of various glucose concentrations, and to establish a feasible method of monitoring the glucose concentration of the solutions. Glycemic cell media solutions were developed and their glucose concentrations were evaluated with a blood glucose meter (specifically, the Aviva Accu-Chek blood glucose meter) or visual blood glucose test strips (Glucoflex R visual blood glucose test strips). The second aim was to ensure that the developed glycemic cell media solutions could be monitored in a cell culture environment over time, and to determine if the hyperglycemic conditions induced any change to endothelial cells. Bovine aortic endothelial cells (BAECs) and human umbilical vein endothelial cells (HUVECs) were used to evaluate glucose consumption and cell morphology. Glucose concentration of the cell media was recorded to evaluate glucose consumption, and the cells were evaluated under a microscope in order to determine cell morphology and an increase in cell death. Results & Conclusions: Data accumulated from the first set of experiments confirmed that glycemic cell media solutions can be developed by adding Sigma G6512 D-(+)-glucose to base cell media. Additionally, the Aviva Accu-Chek blood glucose meter recorded the most accurate and precise glucose concentrations of the various glycemic cell media solutions compared to the Glucoflex-R blood glucose visual test strips. Lastly, the series of experiments with BAECs and HUVECs confirmed that the glycemic cell media solutions could be effectively monitored over time, and that these conditions evoked higher glucose consumption by the endothelial cells compared to the normal glycemic cell media solutions. Additionally, neither glycemic environment evoked significant cell death. These results met the aims of this thesis, and therefore provide the foundation for further development of a diabetic BVM.

Coronary artery disease affects millions of people and the ability to detect and treat the disease is advancing at a rapid rate. As a result, the development of intravascular technologies is the focus of many medical device manufacturers. Specifically, coronary stent implantation is being performed in an increasing number of patients and a number of new stent designs have been introduced to the market, resulting in the need for improved preclinical testing methods. An in vitro tissue engineered “blood vessel mimic” (BVM) system has previously been established and its feasibility for the initial testing of newly emerging intravascular technology has been demonstrated. There are limitations that exist with this original design, however, and the focus of this thesis was to both improve and expand upon the original model. Therefore, research was conducted based on two specific aims. The first aim was to develop a more ideal BVM system to accommodate a wider range of stent lengths and diameters, while allowing for easy graft insertion and seal-ability. The second aim was to develop next generation BVM systems,focused on future needs and technology, such as long, angulated and bifurcated geometries. The work described in this thesis demonstrates that a BVM chamber can be created which has the advantages of easy graft insertion and seal-ability, as well as the ability to accommodate varying sizes of vessel scaffolds, all while maintaining the needs of a tissue engineering bioreactor system. The next generation BVM systems presented demonstrate that the BVM concept can be expanded to meet the needs of long, angulated and bifurcated geometries. Overall, the work in this thesis describes the design and optimization of an in vitro blood vessel mimic bioreactor system for the evaluation of intravascular devices, specifically coronary stents, in simple and complex vessel geometries.

In this thesis, a comprehensive understanding of supervised machine learning algorithms, namely Logistic Regression, Support Vector Machine, Random Forest, and Ensemble Stacking, is performed. This research also extends and further explores different feature selection techniques: correlation analysis, chi-squared, mutual information classification, and Recursive Feature Elimination (RFE). Then, a practical application in the context of coronary artery disease prediction was conducted to apply and analyze models' performance with different feature selection methods on various measures of accuracy, F1 score, and confusion matrix. The outcomes of this experimentation reveal that among models developed, Logistic Regression with chi-squared feature selection is a robust and reliable predictive model, achieving an accuracy of 87.65%. This research advances the understanding of machine learning algorithms and feature selection techniques, with practical implications for reliable prediction of coronary artery disease.

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