Affiliation: Oregon State University, Oregon, USA
E-mail address: Elain.Fu@oregonstate.edu
Elain Fu is an Assistant Professor in Bioengineering at Oregon State University. Prior to this, she spent three years as an Assistant Research Professor in Bioengineering at the University of Washington. Elain received a Sc.B. degree in Physics from Brown University, and M.S. and Ph.D. degrees in Physics from the University of Maryland, College Park. Her research focus has been microfluidics-based sensor development with the goal of using an understanding of the physics and chemistry of device operation to improve device performance for field applications. Recently, she has been active in the area of paper microfluidics. In particular, her lab develops tools for the automated manipulation of reagents in porous materials, as well as specific tests for high-sensitivity detection in low-resource settings for global health applications. She has published over 40 articles in peer-reviewed journals and is a co-inventor on multiple patents and patent applications.
Engineering Paper Microfluidic Sensors for Point-of-Care Applications in Low-Resource Settings
High-sensitivity point-of-care diagnostic assays that are rapid, easy to use, and low cost are needed for use in low-resource settings. The conventional lateral flow test is the standard bioassay format for point-of-care applications in these settings due to its rapid time to result, ease of use, and low cost. However, for many analytes, conventional lateral flow tests lack the sensitivity to have clinical utility. The rapidly growing field of “paper” microfluidics has the potential to address this issue. In particular, we are developing paper-based devices that can automatically perform multi-step sample processing for high-sensitivity detection in a format that is appropriate for use in low-resource settings. Key to the successful operation of these devices is the development of a “paper microfluidics toolbox” for controlling flow and metering multiple small volumes of fluids within devices. This presentation will highlight tools development in the context of applications for infectious disease diagnosis (e.g., malaria and influenza) and therapy monitoring (e.g., for phenylketonuria treatment) in low-resource settings.