| Description |
The creation of new analytical platforms and methodologies is critical to meet the ever-increasing demands for accurate, sensitive, and fast techniques for disease detection and analyte quantitation. Improving the figures of merit (e.g., sensitivity, turnaround time) for an analytical test can lead to significant improvements in patient outcomes, decrease costs, and lead to widespread application of a particular platform. The research in this dissertation details improvements in the figures of merit for three different analytical platforms used to detect disease biomarkers and solid pharmaceutical components. First, an enzyme-linked immunosorbent assay is used to characterize the recovery of a tuberculosis biomarker, mannose-capped lipoarabinomannan (ManLAM), from human serum after the sample has undergone an acid treatment method. In this work, we determined that while acid treatment improves the recovery and detection of ManLAM from human serum, the overall detection is limited by degradation of the marker and incomplete decomplexation from endogenous serum proteins. We then demonstrate an improved recovery relative to acid treatment using an enzymatic treatment method, and that the improvement in ManLAM recovery leads to an improvement in the analytical sensitivity of the TB diagnostic test. Second, a novel, high throughput, and multiprobe Raman spectrometer is used to perform two types of analysis: solid pharmaceutical and surface-enhanced Raman scattering (SERS) immunoassay analysis. Using this instrument, we demonstrate comparable accuracy to standard Raman instrumentation to quantitate the percent composition of solid samples and improved sensitivity for an alpha fetoprotein immunoassay. Futhermore, the analysis time is significantly reduced compared to standard methods by taking six simultaneous measurements. Third, a method to determine the separation distance between a magnetoresisitve sensor and a magnetized nickel address for a magnetics-based immunoassay is detailed. This method analyses the read back signal from the sensor in the frequency domain to create harmonic ratios that can be used to determine the separation distance with submicron resolution. Additionally, a finite-element model was developed to validate the approach and to serve as a predictive tool for new assay substrate configurations. This work concludes by discussing the future prospects and directions towards the development of next generation analytical platforms for detecting analytes related to human health. |
