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This dissertation is composed of two projects dedicated to the development of techniques and technologies for improving the quality of life for patients in both clinical and resource-limited settings. The purpose of the first project was to design a rapid diagnostic device to screen whole blood samples for the presence of infectious agents. Point-of-care (PoC) technologies are becoming increasingly important for the detection of infectious agents in resource-limited settings (RSLs) where state-of-the-art blood screening practices are not feasible for implementation. For this project, a rapid diagnostic device was developed to directly detect pathogen content within freshly drawn whole blood samples using a ligand-binding assay format. The assay is completely self-contained within a hermetically sealed device to minimize operational complexity and ensure operator safety. The diagnostic device is capable of processing complex sample matrices by selectively capturing, concentrating, and labeling infectious agents upon functionalized surfaces. Following sample processing, the assay is optically interrogated with a fluorescence-based reader to provide rapid feedback regarding sample purity. Designs of the rapid diagnostic platform evolved over several prototype generations corresponding to project milestones emphasizing ergonomic performance, military specification testing for environmental resilience, and manufacture to yield production-grade devices for future diagnostic performance data collection. The goal of the regenerative therapy-based portion of this research was to develop a novel technique for the selective enrichment of cells demonstrating enhanced regenerative capacity in tissue-extracted cell samples. Adherent cell cultures of stromal vascular fractions (SVFs) extracted from adipose tissues were exposed to nutrient deficient conditions' eliciting a bimodal cellular response between two dissimilar cell culture subpopulations. The regenerative capacity of the

As is the case with many ocular neuropathies, primary open-angle glaucoma (POAG) leads to an irreversible damage to the visual field. Loss of visual field first occurs in the peripheral vision and slowly propagates towards the middle. Although there are differences in its rate of incidence, glaucoma is projected to be the leading cause of blindness, second only to cataract, affecting significant percentage of populations across different age, race/ethnic groups. A hallmark of POAG is the dysfunction of retinal ganglion cells (RGCs) which connect to the axons which, in turn, relay the visual information from the eye to the brain. Previous research has shown that axonal density in the optic nerve head (ONH) is greatly reduced due to chronically elevated intraocular pressure (IOP). IOP-lowering treatment has been shown to reduce the visual field loss, and continues to be the dominant treatment methodology for glaucoma. Previous research has shown that the biomechanics of the lamina cribrosa (LC) - a highly porous tissue through which the axons carrying the visual information exit the eye, is important in influencing the viability of the RGCs. In a normal eye, the LC is primarily made up of collagen of types I, III, and IV which encompass (specifically, arranged circularly) the axon shafts and the blood vessels (1). In addition to elevated IOP, changes in the material properties of ocular tissues in and around the ONH region, which include peripapillary sclera and LC, could affect its biomechanics, which could be a result of changing microstructure and morphology of these tissues, and may contribute to POAG. The current work is aimed at creating computational models to incorporate the complex nature of ocular tissues, and develop computational techniques to characterize the variation in the material properties of ocular tissues (which include the tissue moduli, fiber orientation, permeability etc.), and study the effects they have on the biomechanical response of the LC

Hip fracture has long been considered as the most serious consequence of osteoporosis, which includes chronic pain, disability, and even death. In the elderly population, a femur fracture is very common. It is assessed that 50% of women aged 50 or older may experience a hip fracture in their remaining life. Hip fracture is among the most common injuries and can lead to substantial morbidity and mortality. In the US alone, over 250,000 hip fractures occur each year and this number is expected to double by the year 2040. Statistics indicate that over 20% of people who experience a hip fracture die within one year and only 25% have a total recovery. Femur fractures are now becoming a major social and economic burden on the health care system. In practice, it is very difficult to predict the femur fracture risks. One of the main reasons is that there is not a robust and easy-to-get measure to quantify the strength of the bone. Clinicians use bone mineral density (BMD) as an indicator of osteoporosis and fracture risk. Several studies showed that BMD cannot be used alone to identify bone strength. In fact, the majority of patients who suffer from fractures have normal or even higher BMD scores. There are a large number of risk factors that contribute to the occurrence of femur fracture, which should also be involved in predicting hip fracture risks. For example, age, weight, height, ethnicity and so on. Some of the factors might not have been identified yet. Thus, there will be a high level of uncertainty in the clinical dataset, which makes it difficult to construct and validate a hip risk prediction model. The objective of the dissertation is to construct an improved hip fracture risk prediction model. Due to the difficulty of obtaining experimental or clinical data, computational simulations might help increase the predictive ability of the risk model. In this research, the hip fracture risk model is based on a support vector machine (SVM) trained using a clinical dat

Abdominal Aortic Aneurysm (AAA) is a complex disease that leads to a localized dilation of the infrarenal aorta that develops over years. Longitudinal information in humans has been difficult to obtain for this disease, therefore mouse models have become increasingly used to study the development of AAAs. The objective of this study was to determine any changes that occur in the biomechanical response and fiber microstructure in the apolipoprotein E difficient (ApoE-/-) angiotensin II (AngII) infused mouse model of aneurysm during disease progression, as well as determine some of the underlying changes in biochemistry, and demonstrate a novel method of reducing any pathogenic protease activity. Using a Microbiaxial Opto-Mechanical Device (MOD), ex vivo studies included adult aortas of ApoE-/- AngII infused mice excised and tested for mechanical response simultaneously imaged using two-photon microscopy to assess the microstructure at multiple time points. In vitro and ex vivo studies have shown changes in protease concentrations with the use of FRET based proteolytic beacons able to provide a non-destructive method to quantify protease activity measured against mechanical and microstructural changes. In vitro studies have demonstrated protease activity can be reduced using a molecule providing a positive feedback mechanism for protease inhibition and possibly provide a reduction in aneurysm progression.

The purpose of this dissertation is to establish a unified theory of porohyperelasticity with transport and growth and to demonstrate the capability of this theory using a finite element model developed in MATLAB. The theory of volumetric growth is combined with the theory of mixed porohyperelasticity with transport and swelling (MPHETS) to derive a new method that models growth of biological soft tissues. The conservation equations and constitutive equations are developed for both solid-only growth and solid-fluid growth. An axisymmetric finite element framework is introduced for the new theory of growing MPHETS (GMPHETS). To demonstrate the difference of the GMPHETS model from a traditional hyperelastic (HE) growth model, several finite element test problems with example growth laws are considered, including time-dependent, concentration-dependent, and stress-dependent growth. In particular, this work demonstrates that the solid-only growth of an MPHETS model of a stylized artery results in a more uniform hoop stress than in a HE model under solid-only growth for the same amount of growth time using the same growth law. This may have implications in the context of developing residual stresses in soft tissues under intraluminal pressure. To my knowledge, this is the first description of an MPHETS model with growth. The developed computational framework can be used together with novel in-vitro and in-vivo experimental approaches to identify the governing growth laws for various soft tissues.

Plaque accumulation in the walls of coronary arteries reduces the delivery of nutrients and oxygen to the myocardium. This luminal narrowing can cause clinical indications such as angina or heart attacks, and without treatment, can be fatal. One method of treatment is the percutaneous intervention of stents to re-canalize the vessel. A potential complication of stent implantation is arterial wall remodeling and renarrowing of the vessel; termed restenosis. This can be prevented in the majority of patients with an antiproliferative drug coating on the surface of the stent: a drug-eluting stent. I hypothesize that drug transport in the arterial wall from these devices varies between arterial locations (left anterior descending (LADC) versus right (RC) coronary artery; proximal, middle, versus distal regions). The purpose of this work was to identify the properties of the vascular wall that govern transport, and computationally model stent-based delivery to better understand any differences that could exist in transport based on location. The first aim of this work was to identify the porohyperelastic properties. Permeability showed a decrease along the length of the LADC artery of 198%, and 98.6% along the length of the RC artery (p=NS between LADC and RC). Mechanical properties indicated significant differences between the LADC and RC arteries, with the LADC artery being stiffer than the RC. The second aim of this work was to identify the mass transport and cellular binding properties. There was no difference between the LADC and RC arteries; however, diffusivity peaked in the middle region of both arteries by a factor of 2.07. Convection coupling coefficients indicated an upward trend down each artery with the RC artery having higher values. The third aim was to use the model constants from the previous two aims to create six parametric computational models of stent deployment and drug delivery into the respective arterial sections. Results indicated that RC section

Abdominal aortic aneurysm (AAA) occurs in developed nations. The current treatment, endovascular aneurysm repair (EVAR), while successful, has shortcomings. A solution to the concerns with EVAR is a polymeric endo-aortic paving (PEAP). Our goal is to further develop this treatment for AAA. We hypothesize that PEAP will overcome the current limitations associated with current AAA repair, while maintaining the desirable qualities of these materials are biocompatibility, thermoformability, and material compliance with aortic tissue. The purpose of this work was to evaluate potential PEAP material candidates, which include acrylate-based shape memory polymers (SMPs), polycaprolactone (PCL) and polyurethane (PU) blends, and finally a PCL-based bioresorbable copolymer poly (ester-urethane-urea) (PEUU). Materials were assessed by characterization of their chemical, thermomechanical and degradation properties. It was determined that acrylate- SMPs were too stiff to be candidates for use in PEAP. The control of the PEUU copolymer composition yielded a material that had increased degradability while maintaining valuable characteristics of the PCL/PU blend prior to, during, and following degradation. The difference between the PU hydrophobicity dictated MTM sensitivity to water immersion and degradation characteristics. Conventional statistics were used to develop three models for predicting key properties of PEUU. Important variables, such as PCL M(n), temperature, and crystallinity were found to impact both copolymer microstructure and mechanical properties.

A biomechanical model of the human tongue was constructed, based upon a detailed anatomical study of an actual cadaver. Data from the Visible Human Project were segmented to create a volumetric representation of the tongue and its constituent muscles. The volumetric representation was converted to a smooth NURBS-bounded solid model--for compatibility with meshing algorithms--by lofting between splines, the vertices of which were defined by the coordinates of a smoothed triangular mesh representation. Using a hyperelastic constitutive model that allowed for the addition of active stress, the model deforms in response to user-specified muscle activation patterns. A series of meshes was created to perform a mesh validation study; in the validation tests performed, a 245,223-element mesh was found to be sufficient to model tongue behavior.Systematic samples of the behavior of the model were collected. Principal component analyses were performed on the samples to discover low-dimensional representations of tongue postures. Statistical models (linear regression models and neural networks) were fit to predict tongue posture from muscle activation, and vice versa. In all tests, it was found that a relatively small sample of tongue postures can be used to successfully generalize to larger data sets.Finally, a variety of specific tests were performed, based on claims and predictions found in previous literature. Of these, the claims of the muscular hydrostat theory of tongue movement were best supported. Simulations were also run that simulated lingual hemiplegia. It was found that substantially different muscular activation patterns were required to achieve equivalent postures in a hemiplegic tongue, relative to a normal tongue.