Year: 2015
Type: dissertation
Abstract: Over the last few decades, lung transplantation (LTx) has become a well-established therapy for patients suffering from end-stage lung disease. Despite the many innovations that have occurred in recent years, the outcomes following LTx still lag behind those of other organ transplant procedures due to a lack of technologies that could monitor predictive biomarkers. Recently, several genomic and proteomic analytes have been identified that are prognostic of LTx outcome; however, traditional analytical methods are impractical for use in the transplant setting. Rapid analysis of molecular biomarkers reporting on the initial and long-term status of a donated organ would improve utilization rates and transplant outcomes. The work herein focuses on developing a novel class of chip-based sensors that would enable direct analysis of mRNAs and proteins that are correlated with the suitability of a lung for transplant. The biosensing approach utilized in these studies was performed using a simple and elegant electrochemical reporter system with metal electrodes of varying morphologies and sizes. By fabricating fractal circuit sensors (FraCS), we demonstrated the technological and biological validation required for a point-of-care (POC) diagnostic device to be used for gene quantitation in the transplant setting. This approach was applied to confirm the clinical validity of previously reported biomarkers (IL-6, IL-10, EGLN1, and ATP11B) and, importantly, was used to develop a FraCS prediction model (FPM), which was highly predictive (AUC 0.82) of primary graft dysfunction (PGD) in donor lungs. Using nanostructured microelectrodes (NMEs), a sensing platform was developed to detect two proteins implicated in chronic lung rejection: TGFβ1 and ET-1 – biomarkers involved in restrictive allograft syndrome (RAS) and bronchiolitis obliterans syndrome (BOS). We highlight novel strategies used for the electrochemical detection of small (
Source: TSpace (University of Toronto)
Author: Andrew T. Sage
Language: English
Subfield: Analytical Chemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: green
Funder: Strong
OpenAlex ID: W2599236443