Browsing by Subject "Early detection"
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Item Development of a cell-based lab-on-a-chip sensor for detection of oral cancer biomarkers(2008-08) Weigum, Shannon Elise; McDevitt, John ThomasOral cancer is the sixth most common cancer worldwide and has been marked by high morbidity and poor survival rates that have changed little over the past few decades. Beyond prevention, early detection is the most crucial determinant for successful treatment and survival of cancer. Yet current methodologies for cancer diagnosis based upon pathological examination alone are insufficient for detecting early tumor progression and molecular transformation. Development of new diagnostic tools incorporating tumor biomarkers could enhance early detection by providing molecular-level insight into the biochemical and cellular changes associated with oral carcinogenesis. The work presented in this doctoral dissertation aims to address this clinical need through the development of new automated cellular analysis methods, incorporating lab-on-a-chip sensor techniques, for examination of molecular and morphological biomarkers associated with oral carcinogenesis. Using the epidermal growth factor receptor (EGFR) as a proof-of-principle biomarker, the sensor system demonstrated capacity to support rapid biomarker analysis in less than one-tenth the time of traditional methods and effectively characterized EGFR biomarker over-expression in oral tumor-derived cell lines. Successful extension from in vitro tumor cell lines to clinically relevant exfoliative brush cytology was demonstrated, providing a non-invasive method for sampling abnormal oral epithelium. Incorporation of exfoliative cytology further helped to define the important assay and imaging parameters necessary for dual molecular and morphological analysis in adherent epithelium. Next, this new sensor assay and method was applied in a small pilot study in order to secure an initial understanding of the diagnostic utility of such biosensor systems in clinical settings. Four cellular features were identified as useful indicators of cancerous or pre-cancerous conditions including, the nuclear area and diameter, nuclear-to-cytoplasm ratio, and EGFR biomarker expression. Further examination using linear regression and ROC curve analysis identified the morphological features as the best predictors of disease while a combination of all features may be ideal for classification of OSCC and pre-malignancy with high sensitivity and specificity. Further testing in a larger sample size is necessary to validate this regression model and the LOC sensor technique, but shows strong promise as a new diagnostic tool for early detection of oral cancer.Item Improving surveillance and prediction of emerging and re-emerging infectious diseases(2019-08) Liu, Kai (Ph. D. in cell and molecular biology); Meyers, Lauren Ancel; Wilke, Claus O.; Bull, James J.; Hillis, David; Scott, JamesInfectious diseases are emerging at an unprecedent rate in recent years, such as the flu pandemic initialized from Mexico in 2009, the 2014 Ebola epidemic in West Africa, and the 2016-2017 expansion of Zika across Americas. They rarely happened previously and thus lack resources and data to detect and predict their spread. This highlights the challenges in emerging an re-emerging infectious disease surveillance. In the dissertation, I mainly put efforts in developing methods for early detection of such diseases, and assessing predictive power of various models in early phase of an epidemic. In Chapter 2, I developed a two-layer early detection framework which provides early warning of emerging epidemics based on the idea of anomaly detection. The framework could evaluate and identify data sources to achieve the best performance automatically from available data, such as data from the Internet and public health surveillance systems. I demonstrated the framework using historical influenza data in the US, and found that the optimal combination of predictors includes data sources from Google search query and Wikipedia page view. The optimized system is able to detect the onset of seasonal influenza outbreaks an average of 16.4 weeks in advance, and the second wave of the 2009 flu pandemic 5 weeks ahead. In Chapter 3, I extended the framework in Chapter 2 to identify large dengue outbreaks from small ones. The results show that the framework could personalize optimal combinations of predictors for different locations, and an optimal combination for one location might not perform well for other locations. In Chapter 4, I investigated the contribution of different population structures to total epidemic incidence, peak intensity and timing, and also explored the ability of various models with different population structures in predicting epidemic dynamics. The results suggest that heterogeneous contact pattern and direct contacts dominate the evolution of epidemics, and a homogeneous model is not able to provide reliable prediction for an epidemic. In summary, my dissertation not only provides method frameworks for building early detection systems for emerging and re-emerging infectious diseases, but also gives insight to the effects of various models in predicting epidemics.Item Microelectromechanical handheld laser-scanning confocal microscope: application to breast cancer imaging(2009-05) Kumar, Karthik; Neikirk, Dean P.; Zhang, Xiaojing, Ph.D.Demographic data indicate that 60% of 6.7 million annual global cancer mortalities and 54% of 10.8 million new patients are in developing nations, unable or unwilling to avail of invasive screening tests that are the current norm. For most cancers, survival rate is strongly dependent on early detection, highlighting the need for improved screening methods. Studies have shown that cancers can be identified based on distinct sub-cellular morphological features and expression levels of specific molecular markers. Since 85% of cancers are known to originate in the epithelium, portable in vivo imaging techniques providing sub-cellular detail in tissue up to depths of 250 μm could help improve access to biopsy-free examination in low-infrastructure environments. The resultant early detection could dramatically improve patient prognosis, while reducing screening costs, treatment delay, and occurrences of unnecessary and potentially harmful medication. This dissertation investigates handheld instrumentation for laser-scanning confocal microscopy (LSCM) and its applicability to breast cancer detection and subsequent image-guided management. LSCM allows high-resolution mapping of spatial variations in refractive index or tumor marker expression within a single cell layer situated few hundred micrometers beneath the tissue surface. The main challenge facing miniaturization lies in the mechanism of beam deflection across the sample. The first part of the dissertation presents a fast, large-angle, high-reflectivity two-axis vertical comb driven silicon micromirror fabricated by a novel method compatible with complementary metal-oxide-semiconductor processing employed in the semiconductor industry. The process enables integration of rotation sensors on the chip to adaptively correct for aberrations in beam scanning while significantly reducing fabrication costs and barriers to market acceptance. The second part of the dissertation explores the integration of this micromirror with other optical and electronic components into a handheld laser-scanning confocal microscope. Applicability of the probe to epithelial breast cancer screening via reflectance and fluorescence imaging is investigated. Finally, enhanced imaging modalities based on the micromirror are presented. 3D cellular-level in vivo imaging via rapid swept-source optical coherence tomography is demonstrated. A method for “objective-less” microendoscopy, potentially resulting in substantially reduced probe dimensions, employing reflective binary-phase Fresnel zone plates monolithically integrated on the surface of the micromirror is presented.