Rapid, Affordable, and Point-of-Care Water Monitoring Via a Microfluidic DNA Sensor and a Mobile Interface for Global Health
Contaminated water is a serious concern in many developing countries with severe health consequences particularly for children. Current methods for monitoring waterborne pathogens are often time consuming, expensive, and labor intensive, making them not suitable for these regions. Electrochemical detection in a microfluidic platform offers many advantages such as portability, minimal use of instrumentation, and easy integration with electronics. In many parts of the world, however, the required equipment for pathogen detection through electrochemical sensors is either not available or insufficiently portable, and operators may not be trained to use these sensors and interpret results, ultimately preventing its wide adoption. Counterintuitively, these same regions often have an extensive mobile phone infrastructure, suggesting the possibility of integrating electrochemical detection of bacterial pathogens with a mobile platform. Toward a solution to water quality interventions, we demonstrate a microfluidic electrochemical sensor combined with a mobile interface that detects the sequences from bacterial pathogens, suitable for rapid, affordable, and point-of-care water monitoring. We employ the transduction of DNA hybridization into a readily detectable electric signal by means of a conformational change of DNA stem-loop structure. Using this platform, we successfully demonstrate the detection of as low as 100 nM E. coli sequences and the automatic interpretation and mapping of the detection results via a mobile application.
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See complete bios of the authors in the full version of this article.
Dr. Kim is an Assistant Professor of bioengineering with Santa Clara University, Santa Clara, CA, USA, where she serves as the Director of the Biological Micro/Nanosystems Laboratory. Her research interests involve the investigation of integrated microfluidic systems to address challenging needs in the biomedical applications.
Ms. Ghanbari has a B.S. degree in bioengineering from Santa Clara University and since 2012, she has been with Life Technologies, focusing on digital PCR. Her research interests include DNA detection and diagnostics.
Ms. Avikumar has a B.S. degree in bioengineering from Santa Clara University. Her current research interests include medical device, bioimaging and signal processing, and Bio-MEMS.
Mr. Seubert has B.S. and M.S. degrees in computer science and engineering from Santa Clara University. His main area of research is mobile computing, and he has experience in mobile development for emerging markets.
Dr. Figueira has a Ph.D. in computer science from the University of California, San Diego. Currently, she is an Associate Professor of computer engineering with Santa Clara University, Santa Clara, CA, USA. Her research is in the area of performance evaluation and prediction, recently with a focus on energy efficiency.
The intersection between microfluidic point-of-care technology and diagnosis represents the bridge between the state-of-the-art standards and the developing world–essentially a democratization of cutting edge medical technology. This article exemplifies one of the extraordinary number of applications microfluidic immunosensing platforms have, from the depths of disease detection such as cancers to universal necessities such as water monitoring as comprehensively discussed in this article. This platform that is used in this microfluidic environment with its electrochemical detection of sequence specific bacterial pathogens can be applied to protein detection of specific biomarkers of diseases. By making this platform point-of-care, the standards of portability, cost-effectiveness, and scalability are inherently met–demonstrating a leap ahead of conventional laboratory testing. More importantly, this article brings another innovation to the table by putting the test on mobile devices, which holistically increases the accuracy of the analysis–since it no longer necessitates the end-user to interpret results. The results and observations gained from this research can provide a paradigm in which future endeavors on point-of-care diagnosis can follow.
This article appeared in the 2013 issue of IEEE Journal of Translational Engineering in Health and Medicine.
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