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In this research, a proton exchange membrane fuel cell (PEMFC) sensor was investigated for specific detection of volatile organic compounds (VOCs) for point-of-care (POC) diagnosis of the physiological conditions of humans. A PEMFC is an electrochemical transducer that converts chemical energy into electrical energy. A Redox reaction takes place at its electrodes whereas the volatile biomolecules (e.g. ethanol) are oxidized at the anode and ambient oxygen is reduced at the cathode. The compounds which were the focus of this investigation were ethanol (C2H5OH) and isoflurane (C3H2ClF5O), but theoretically, the sensor is not limited to only those VOCs given proper calibration. Detection in biosensing, which needs to be carried out in a controlled system, becomes complex in a multivariate environment. Major limitations of all types of biosensors would include poor selectivity, drifting, overlapping, and degradation of signals. Specific detection of VOCs in multi-dimensional environments is also a challenge in fuel cell sensing. Humidity, temperature, and the presence of other analytes interfere with the functionality of the fuel cell and provide false readings. Hence, accurate and precise quantification of VOC(s) and calibration are the major challenges when using PEMFC biosensor. To resolve this problem, a statistical model was derived for the calibration of PEMFC employing multivariate analysis, such as the “Principal Component Regression (PCR)” method for the sensing of VOC(s). PCR can correlate larger data sets and provides an accurate fitting between a known and an unknown data set. PCR improves calibration for multivariate conditions as compared to the overlapping signals obtained when using linear (univariate) regression models. Results show that this biosensor investigated has a 75% accuracy improvement over the commercial alcohol breathalyzer used in this study when detecting ethanol. When detecting isoflurane, this sensor has an average deviation in the stead

The contamination of the environment, accidental or intentional, in particular with chemical toxins such as industrial chemicals and chemical warfare agents has increased public fear. There is a critical requirement for the continuous detection of toxins present at very low levels in the environment. Indeed, some ultra-sensitive analytical techniques already exist, for example chromatography and mass spectroscopy, which are approved by the US Environmental Protection Agency for the detection of toxins. However, these techniques are limited to the detection of known toxins. Cellular expression of genomic and proteomic biomarkers in response to toxins allows monitoring of known as well as unknown toxins using Polymerase Chain Reaction and Enzyme Linked Immunosensor Assays. However, these molecular assays allow only the endpoint (extracellular) detection and use labels such as fluorometric, colorimetric and radioactive, which increase chances of uncertainty in detection. Additionally, they are time, labor and cost intensive. These technical limitations are unfavorable towards the development of a biosensor technology for continuous detection of toxins. Federal agencies including the Departments of Homeland Security, Agriculture, Defense and others have urged the development of a detect-to-protect class of advanced biosensors, which enable environmental surveillance of toxins in resource-limited settings. In this study a Surface-Enhanced Raman Spectroscopy (SERS) immunosensor, aka a SERS-linked immunosensor assay (SLISA), has been developed. Colloidal silver nanoparticles (Ag NPs) were used to design a flexible SERS immunosensor. The SLISA proof-of-concept biosensor was validated by the measurement of a dose dependent expression of RAD54 and HSP70 proteins in response to H2O2 and UV. A prototype microchip, best suited for SERS acquisition, was fabricated using an on-chip SLISA to detect RAD54 expression in response to H2O2. A dose-response relationship between H2O2 and

Microcirculatory vessels are lined by endothelial cells (ECs) which are surrounded by a single or multiple layer of smooth muscle cells (SMCs). Spontaneous and agonist induced spatiotemporal calcium (Ca2+) events are generated in ECs and SMCs, and regulated by complex bi-directional signaling between the two layers which ultimately determines the vessel tone. The contractile state of microcirculatory vessels is an important factor in the determination of vascular resistance, blood flow and blood pressure. This dissertation presents theoretical insights into some of the important and currently unresolved phenomena in microvascular tone regulation. Compartmental and continuum models of isolated EC and SMC, coupled EC-SMC and a multi-cellular vessel segment with deterministic and stochastic descriptions of the cellular components were developed, and the intra- and inter-cellular spatiotemporal Ca2+ mobilization was examined. Coupled EC-SMC model simulations captured the experimentally observed localized subcellular EC Ca2+ events arising from the opening of EC transient receptor vanilloid 4 (TRPV4) channels and inositol triphosphate receptors (IP3Rs). These localized EC Ca2+ events result in endothelium-derived hyperpolarization (EDH) and Nitric Oxide (NO) production which transmit to the adjacent SMCs to ultimately result in vasodilation. The model examined the effect of heterogeneous distribution of cellular components and channel gating kinetics in determination of the amplitude and spread of the Ca2+ events. The simulations suggested the necessity of co-localization of certain cellular components for modulation of EDH and NO responses. Isolated EC and SMC models captured intracellular Ca2+ wave like activity and predicted the necessity of non-uniform distribution of cellular components for the generation of Ca2+ waves. The simulations also suggested the role of membrane potential dynamics in regulating Ca2+ wave velocity. The multi-cellular vessel segment model exa