Portable Graphene Biosensors Could Help Predict Zika Transmission
The weather is warming up, and the topic has turned again to staying safe while having fun outdoors. Specifically, how do we avoid mosquito bites with the dangerous diseases that they potentially carry? Nanomedical Diagnostics has always had an interest in the potential of graphene biosensors as portable diagnostic devices designed to help healthcare providers make informed, immediate decisions. In fact, last year we published a paper in Biosensors and Bioelectronics titled “Novel Graphene-Based Biosensor for Early Detection of Zika Virus Infection.” While a mass-manufactured product to do this is still in the development phase, we are working side-by-side with researchers advancing this and similar applications for the clinical space.
To that end, we were excited to see a conference proceedings paper recently published for the 2018 International Conference on Digital Health held in Lyon, France titled “ZIKɅ : A New System to Empower Health Workers and Local Communities to Improve Surveilance Protocols by E-Learning and to Forecast Zika Virus in Real Time in Brazil.” Beltrán, JD et al describe their work developing the ZIKɅ system, which couples public health surveillance data with on-the-ground health worker reports to provide data sets that predict the spread of Zika virus in real-time. This system is currently being tested in Brazil, one of the areas hardest hit by the Zika outbreak.
So, how do graphene biosensors help? Time is a critical component to the effectiveness of the ZIKɅ system, and by the time health workers at rural clinics have taken blood samples, sent them to distant labs, and received results back, days have gone by. Beltrán, JD et al propose testing a modified clinical version of Agile R100 as a potential solution to the long turnaround time, writing that “the use of a cost-effective and portable graphene-enabled biosensor…will help reach remote areas that could be in high risk.” With this data, they can validate and enrich their computer models, which in the end could provide robust forecasting to help stop the spread of the Zika virus.