CAREER: Unified sample preparation and micro-scale centrifugal heating for the next generation of portable and inexpensive molecular diagnostic platform

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The recent global pandemic has exposed some of the key limitations in our current health management system, particularly when applied in remote areas. Existing approaches for disease assessment are highly resource-intensive, and this introduces a considerable time lag between sample collection, diagnosis, and implementation of countermeasures. A need, therefore, exists for inexpensive and robust tools that can be broadly deployed to accelerate diagnosis, and provide real-time data to better inform decision making. This CAREER project focuses on developing a new class of simple, inexpensive, portable, and rapid molecular diagnostic platforms with the potential for widespread deployment by adapting a PCR-like test from dedicated laboratories to resource-limited places where they are needed the most. It combines insights from biochemistry, magnetism, fluid physics, and machine learning to engineer an instrument that can reliably and quickly detect infectious diseases in human samples. The project’s educational component integrated with the research includes the development of molecular biology-focused education modules and a series of interactive and explanatory YouTube videos describing scientific concepts in biochemistry, magnetism, and fluid mechanics.One of the most pressing issues facing global public health today is the lack of accessible, affordable, and simple-to-use diagnostic technologies. The goal of this CAREER project is to introduce several innovations towards engineering a novel molecular diagnostics system. First, a new biophysical model will be developed to predict Loop-Mediated Isothermal Amplification (LAMP) - a widely popular but poorly understood nucleic acid amplification reaction. Insights from the model will be used to thermodynamically optimize LAMP primer sets to yield rapid amplification of pathogenic target (10 minutes) and offer fewer false positives. Second, a new mechanism for microfluidic heating will be explored where small fluidic volumes on spinning discs can be heated by converting some of the disc's rotational kinetic energy into thermal energy via magnetically induced eddy currents. Third, machine learning based image classifiers will be developed that will enable smartphone cameras to perform real time temperature sensing (using color sensitive dyes) and quantitative molecular assay readouts with high reproducibility and robustness. Taken together, the resulting molecular diagnostic platform can deliver performance comparable to that of current generation systems while simultaneously providing an order of magnitude reduction in turnaround time, cost, and device complexity.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.