Myocardial infarction (MI) remains the leading cause of mortality and morbidity in the U.S., accounting for nearly 140,000 deaths per year. Heart transplantation and implantation of mechanical assist devices are the options of last resort for intractable heart failure, but these are limited by lack of organ donors and potential surgical complications. In this regard, there is an urgent need for developing new effective therapeutic strategies to induce regeneration and restore the loss contractility of infarcted myocardium. Over the past decades, regenerative medicine has emerged as a promising strategy to develop scaffold-free cell therapies and scaffold-based cardiac patches as potential approaches for MI treatment. Despite the progress, there are still critical shortcomings associated with these approaches regarding low cell retention, lack of global cardiomyocytes (CMs) synchronicity, as well as poor maturation and engraftment of the transplanted cells within the native myocardium. The overarching objective of this dissertation was to develop two classes of nanoengineered cardiac patches and scaffold-free microtissues with superior electrical, structural, and biological characteristics to address the limitations of previously developed tissue models. An integrated strategy, based on micro- and nanoscale technologies, was utilized to fabricate the proposed tissue models using functionalized gold nanomaterials (GNMs). Furthermore, comprehensive mechanistic studies were carried out to assess the influence of conductive GNMs on the electrophysiology and maturity of the engineered cardiac tissues. Specifically, the role of mechanical stiffness and nano-scale topographies of the scaffold, due to the incorporation of GNMs, on cardiac cells phenotype, contractility, and excitability were dissected from the scaffold’s electrical conductivity. In addition, the influence of GNMs on conduction velocity of CMs was investigated in both coupled and uncoupled gap junctions using microelectrode array technology. Overall, the key contributions of this work were to generate new classes of electrically conductive cardiac patches and scaffold-free microtissues and to mechanistically investigate the influence of conductive GNMs on maturation and electrophysiology of the engineered tissues.

Objectives: Prediabetes is a major epidemic and is associated with adverse cardio-cerebrovascular outcomes. Early identification of patients who will develop rapid progression of atherosclerosis could be beneficial for improved risk stratification. In this paper, we investigate important factors impacting the prediction, using several machine learning methods, of rapid progression of carotid intima-media thickness in impaired glucose tolerance (IGT) participants.

Methods: In the Actos Now for Prevention of Diabetes (ACT NOW) study, 382 participants with IGT underwent carotid intima-media thickness (CIMT) ultrasound evaluation at baseline and at 15–18 months, and were divided into rapid progressors (RP, n = 39, 58 ± 17.5 μM change) and non-rapid progressors (NRP, n = 343, 5.8 ± 20 μM change, p < 0.001 versus RP). To deal with complex multi-modal data consisting of demographic, clinical, and laboratory variables, we propose a general data-driven framework to investigate the ACT NOW dataset. In particular, we first employed a Fisher Score-based feature selection method to identify the most effective variables and then proposed a probabilistic Bayes-based learning method for the prediction. Comparison of the methods and factors was conducted using area under the receiver operating characteristic curve (AUC) analyses and Brier score.

Results: The experimental results show that the proposed learning methods performed well in identifying or predicting RP. Among the methods, the performance of Naïve Bayes was the best (AUC 0.797, Brier score 0.085) compared to multilayer perceptron (0.729, 0.086) and random forest (0.642, 0.10). The results also show that feature selection has a significant positive impact on the data prediction performance.

Conclusions: By dealing with multi-modal data, the proposed learning methods show effectiveness in predicting prediabetics at risk for rapid atherosclerosis progression. The proposed framework demonstrated utility in outcome prediction in a typical multidimensional clinical dataset with a relatively small number of subjects, extending the potential utility of machine learning approaches beyond extremely large-scale datasets.