Advancing Point-of-Care Diagnostics: Automated, High-throughput, Microfluidic Platforms for Autoimmune Disease Biomarker Detection

Abstract

Rheumatoid arthritis (RA), a chronic autoimmune disease, demands early diagnosis to prevent irreversible joint damage and improve patient outcomes. However, current diagnostic methods often suffer from delays, high costs, and limited accessibility, particularly in resource-constrained settings. This thesis presents a pioneering approach to address these challenges through the development of advanced point-of-care (POC) diagnostic platforms, integrating electrochemical biosensing with microfluidics to enable rapid, sensitive, and cost-effective detection of RA biomarkers.

The research introduces three innovative technologies, progressively building from component-level advancements to a fully integrated system. First, a high-gradient magnetophoretic bead trapping technique enhances analyte concentration, achieving a threefold increase in capture efficiency and a limit of detection (LOD) of 4 pg/mL for IL-6, a key inflammatory biomarker. Second, a flexible multiplexed electrochemical biosensor, modified with reduced graphene oxide and gold nanoparticles, improves signal detection and adaptability for conformal integration. Third, the RAVELISA platform, a vacuum-driven microfluidic system, automates high-throughput analysis of multiple biomarkers, processing 9 samples across 16 targets simultaneously with an LOD of 0.89 pg/mL, comparable to conventional laboratory-based ELISA, while reducing assay time by 50% and production costs by 60-70%.

These advancements leverage scalable fabrication methods and commercially available materials, ensuring practical applicability. The platforms demonstrate exceptional sensitivity, multiplexing capability, and user-friendliness, addressing critical gaps in RA diagnostics. By facilitating early detection, this work has the potential to transform clinical practice, reduce the socioeconomic burden of RA, and pave the way for future clinical validation and deployment in diverse healthcare settings.

Date of Award	2025
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology
Supervisor	George Jie YUAN (Supervisor)