Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item MECHANICAL CHARACTERIZATION OF NORMAL AND CANCEROUS BREAST TISSUE SPECIMENS USING ATOMIC FORCE MICROSCOPY(2014) Roy, Rajarshi; Desai, Jaydev P.; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Breast cancer is one of the most common malignancies among women worldwide. Conventional breast cancer diagnostic methods involve needle-core biopsy procedures, followed by careful histopathological inspection of the tissue specimen by a pathologist to identify the presence of cancerous lesions. However, such inspections are primarily qualitative and depend on the subjective impressions of observers. The goal of this research is to develop approaches for obtaining quantitative mechanical signatures that can accurately characterize malignancy in pathological breast tissue. The hypothesis of this research is that by using contact-mode Atomic Force Microscopy (AFM), it is possible to obtain differentiable measures of stiffness of normal and cancerous tissue specimens. This dissertation summarizes research carried out in addressing key experimental and computational challenges in performing mechanical characterization on breast tissue. Firstly, breast tissue specimens studied were 600 um in diameter, about six times larger than the range of travel of conventional AFM X-Y stages used for imaging applications. To scan tissue properties across large ranges, a semi automated image-guided positioning system was developed that can be used to perform AFM probe-tissue alignment across distances greater than 100 um at multiple magnifications. Initial tissue characterization results indicate that epithelial tissue in cancer specimens display increased deformability compared to epithelial tissue in normal specimens. Additionally, it was also observed that the tissue response depends on the patient from whom the specimens were acquired. Another key challenge addressed in this dissertation is accurate data analysis of raw AFM data for characterization purposes. Two sources of uncertainty typically influence data analysis of AFM force curves: the AFM probe's spring constant and the contact point of an AFM force curve. An error-in-variable based Bayesian Changepoint algorithm was developed to quantify estimation errors in the tissue's elastic properties due to these two error sources. Next, a parametric finite element modeling based approach was proposed in order to account for spatial heterogeneity in the tissue response. By using an exponential hyperelastic material model, it was shown that it is possible to obtain more accurate material properties of tissue specimens as opposed to existing analytical contact models. The experimental and computational strategies proposed in this dissertation could have a significant impact on high-throughput quantitative studies of biomaterials, which could elucidate various disease mechanisms that are phenotyped by their mechanical signatures.Item Mechanical Manipulation and Characterization of Biological Cells(2008-10-07) Pillarisetti, Anand; Desai, Jaydev P; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Mechanical manipulation and characterization of an individual biological cell is currently one of the most exciting research areas in the field of medical robotics. Single cell manipulation is an important process in intracytoplasmic sperm injection (ICSI), pro-nuclei DNA injection, gene therapy, and other biomedical areas. However, conventional cell manipulation requires long training and the success rate depends on the experience of the operator. The goal of this research is to address the drawbacks of conventional cell manipulation by using force and vision feedback for cell manipulation tasks. We hypothesize that force feedback plays an important role in cell manipulation and possibly helps in cell characterization. This dissertation will summarize our research on: 1) the development of force and vision feedback interface for cell manipulation, 2) human subject studies to evaluate the addition of force feedback for cell injection tasks, 3) the development of haptics-enabled atomic force microscope system for cell indentation tasks, 4) appropriate analytical model for characterizing the mechanical property of mouse embryonic stem cells (mESC) and 5) several indentation studies on mESC to determine the mechanical property of undifferentiated and early differentiating (6 days under differentiation conditions) mESC. Our experimental results on zebrafish egg cells show that a system with force feedback capability when combined with vision feedback can lead to potentially higher success rates in cell injection tasks. Using this information, we performed experiments on mESC using the AFM to understand their characteristics in the undifferentiated pluripotent state as well as early differentiating state. These experiments were done on both live as well as fixed cells to understand the correlation between the two during cell indentation studies. Our results show that the mechanical property of undifferentiated mESC differs from early differentiating (6th day) mESC in both live and fixed cells. Thus, we hypothesize that mechanical characterization studies will potentially pave the way for developing a high throughput system with force feedback capability, to understand and predict the differentiation path a particular pluripotent cell will follow. This finding could also be used to develop improved methods of targeted cellular differentiation of stem cells for therapeutic and regenerative medicine.