Browsing by Author "McDevitt, John T"

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    Advancement of an agarose bead-based immunoassay platform towards point-of-care applications
    (2015-12-04) Kulla, Eliona; McDevitt, John T
    Efficient and affordable technologies for early detection of disease are of high priority in modern practice of medicine in order to effectively manage diseases and improve outcomes. The programmable bio-nano-chip (p-BNC) platform is an in-development miniaturized micro-device aimed for broad-scale clinical practice at the point-of-care which utilizes agarose beads as a solid-support surface for immunoassay testing in a lab-on-a-chip format. This dissertation work describes research directed towards advancing the p-BNC platform. It begins by introducing a new custom-designed flow-through biochip which holds the agarose beads in an increased pressure-driven flow environment, allowing for an enhancement in analyte capture inside the three-dimensional fibrous network of agarose. This part of the thesis addresses a major concern of how to further lower limits of detection for quantitative measurements of samples composed of low concentrations of biomarkers while using inexpensive, disposable biochips. The thesis next presents novel experimental work exploring the use of agarose beads with an extended range of porosity to maximize binding capacity for three biomarkers of varying molecular weights and sizes. This work demonstrates the importance of pores in the agarose solid support structure for increasing analyte binding capacity and sensitivity, as well as showed that agarose beads with one pore size are unsuitable for all biomarker sizes. Next, a novel extension of the p-BNC system for measuring the binding kinetics of antibodies is demonstrated which reveals the importance of binding strength for optimizing immunoassay sensitivity and dynamic range. Lastly, long-term stability of antibody-functionalized agarose beads stored in the p-BNC system is examined. Here, it is demonstrated for the first time that, by stabilizing with polyhydoxyl compounds, agarose beads can be stored in wet form in the p-BNC device at room temperature for up to 2 months while maintaining their structural integrity and biological function.
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    Development of a "Cytology-on-Chip" Sensor for Monitoring Potentially Malignant Oral Lesions
    (2016-04-21) Abram, Timothy; McDevitt, John T
    The poor prognosis associated with oral cancer, which has progressed into a global epidemic, is attributed to late-stage diagnosis, multi-focal involvement, and a lack of prognostic indicators for identifying which lesions will undergo malignant transformation. Scalpel biopsy combined with histopathological evaluation remains the gold standard in oral cancer diagnosis and monitoring, but this invasive procedure suffers from severe limitations including poor inter-pathologist agreement and an inability to predict patient outcomes. Furthermore, surgical excision of oral lesions is not capable of completely eliminating the risk of malignant transformation; hence, patients are subjected to repeat biopsy as the sole means of monitoring disease progression and recurrence. Oral medicine clinicians urgently need new technologies that can afford non-invasive, sensitive, and quantitative risk assessments for monitoring the progression of pre-malignant oral lesions. This dissertation describes the development of a “cytology-on-chip” sensor combining non-invasive sampling, microfluidic sample processing and single-cell interrogation, and adaptive machine learning algorithms toward the ultimate goal of empowering oral medicine and dental practitioners to monitor subtle changes at the molecular and cellular levels of suspicious oral lesions for personalized disease management. The use of an “enhanced gold standard” to increase the confidence in histopathological grading for 775 prospectively-recruited patients with potentially malignant oral lesions is demonstrated, resulting in increased pathologist agreement from 69% to 100%. Single-cell measurements from a panel of molecular biomarker assays for each patient were then used to train and validate robust risk stratification models with an overall accuracy of 70%. Two strategies for improving future access to this approach are discussed, including the design of a scalable, pumpless microfluidic device to enable parallel assay processing and the adaptation of a fully-integrated lab-on-a-chip system for future chair-side testing. Equivalency to previous methods was indicated for both systems, ensuring consistent functionality. Finally, the realization of a continuous risk index is detailed, resulting in an improvement in overall predictive accuracy to 79.2% and with strong potential to monitor disease severity over time. A proof-of-concept of the ability to inform clinical decision making with such a tool was performed on a high-risk patient population that successfully identified high-risk oral lesions.
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    Integrated Instrumentation and Multivariate Index Assay System for Cardiovascular Health Assessment at the Point-of-Care
    (2016-04-20) McRae, Michael; McDevitt, John T
    Cardiovascular disease (CVD) is the leading cause of death in the United States and globally, accounting for approximately one in three deaths. Early detection and frequent monitoring of traditional risk factors and biomarkers is necessary to save lives and reduce unnecessary costs due to CVD morbidity and mortality. This dissertation aims to develop new tools and processes that can contribute more effective means to assist in prevention and early intervention at the point-of-care through integrated instrumentation and predictive models for CVD. The Programmable Bio-Nano-Chip (p-BNC) is a platform to digitize biology in which small quantities of patient sample generate immunofluorescent signal that is optically extracted and converted to antigen concentrations. A novel fluid delivery method was developed to provide accurate and repeatable flow rates via actuation of the p-BNC cartridge’s blister packs. A portable analyzer instrument was designed and fabricated so as to facilitate fluid delivery, optical detection, automated data analysis, and intuitive mobile health interfaces, representing a universal system for acquiring, processing, and managing clinical data streams and overcoming many of the challenges facing the widespread clinical adoption of lab-on-a-chip technologies. The p-BNC system is also a biosensor system that learns—a diagnostic device that implements machine-learning algorithms for diagnosis and prognosis for a variety of disease applications. In this capacity, a Cardiac ScoreCard system was developed to predict a spectrum of CVD using multiplexed biomarker measurements, symptoms, medical history, and demographics. The combination of a platform to digitize biology and predictive analytics has the potential to alter the trajectory of medicine, where the current linear thinking—mainly based on late-stage disease diagnosis using expensive and cumbersome tools—is replaced by a pathway to exponential medicine made possible through the introduction of scalable tools with the capacity to learn.