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Smartphone app to detect Zika, dengue in 30 minutes

Sentinel Digital DeskBy : Sentinel Digital Desk

  |  22 March 2017 12:00 AM GMT

New York, March 21: Researchers including one of Indian origin have developed a smartphone-controlled, battery-operated diagnostic device that costs as little as $100 and claims to detect Zika, dengue and chikungunya within 30 minutes.
The device, developed by the team at Sandia tiol Labs, part of the US Department of Energy, is based on the loop-mediated isothermal amplification (LAMP) diagnostic method which elimites the need to process a biological sample such as blood or urine, before testing.
“In addition to creating an app that serves as a simple interface to operate the device, we were able to adapt smartphone camera sensors to replace traditiol laboratory sample alysis tools, allowing for unprecedented mobility,” said Aashish Priye, chemical engineer and lead author of the paper. LAMP also elimites the need for extra sample preparation before testing.
“We’ve demonstrated that the chemistry we’re using can amplify viral R directly from raw, unprocessed samples,” said Sandia chemical engineer and project lead Robert Meagher.
Meagher and his team developed a technique known as quenching of unincorporated amplification sigl reporters (QUASR), that involves tagging fragments of synthesised viral D called primers with fluorophores — molecules that emit bright light sigls.
QUASR then causes samples containing viral D/R to appear bright, while negative reactions remain dark. For the Zika project, Meagher’s team developed a novel algorithm that allows a smartphone sensor to act as a fluorimeter, detecting QUASR LAMP light sigls if they appear.
LAMP works so simply that the user need only place the smartphone on top of the LAMP box and open an app. The app turns on the heater to initiate the LAMP reaction. This smartphone-based image alysis offers much greater detection certainty than the lab technician’s ked eye, the authors said. By ebling diagnosis in 30 minutes, the device could help clinicians make faster decisions about patient care and isolation, and rapidly alert public health authorities so they can take measures to prevent spread of the virus. (IANS)

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