Electrophotonic Silicon Biosensor Speeds Up Blood Test Analyses

The complexity and heterogeneity of many diseases, and their dependence on lifestyle and genetics, demand new in vitro diagnostic technology that can provide quantitative measurement of multiple disease biomarkers in real time and at low cost.

Scientists at the University of York (Heslington, UK) have developed a biosensor that combines light and electricity, to detect multiple disease biomarkers in one smaller sample of blood. The technology could make blood tests more comfortable for patients and enable results to be processed quicker. The team is using the new technology in urine samples for urinary tract infections (UTIs), which has a high resistance to antibiotic treatment. If the biosensor can identify biomarkers of the infection and of resistance, it is more likely that the correct course of antibiotic treatment will be prescribed the first time around, rather than on repeat visits, which is often the case with UTIs.

The investigators created a device capable of multiplexed detection that consists of a pair of photonic electrode ring resonators in the same microfluidic chamber. To explore the dual sensing capabilities of the device, they used the electrophotonic technology to monitor and quantify electrochemical reactions occurring at the silicon surface. The combination of electrochemical and photonic sensing not only provides access to complementary information, but also the ability to regulate the local surface chemistry via electrochemical processes in situ.

Thomas F Krauss, PhD, a professor and co-author of the study, said, “These sensors can give fast, real time results and at low cost. The length of time and money that it takes laboratory technicians to identify just one protein in a patient sample is a real challenge for the UK National Health Service (NHS) and can result in emotional distress for patients. Not only can this new technology speed the process up, but it can test for a number of proteins and enzymes together in just one sample, increasing the chances of a successful and timely diagnosis.” The study was published on September 14, 2016, in the journal Nature Communications.

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