Researchers at the National University of Singapore (NUS) have created a portable diagnostic kit called enVision for the quick and accurate screening of a variety of diseases.

The researchers incorporated the new test platform with DNA molecular machines that have the capability to identify the genetic material of different diseases and perform various functions.

“enVision requires 30 minutes to one hour to detect the diseases, which is considered two to four times faster than existing infection diagnostics methods.”

The point-of-care device can be used to diagnose emerging infectious diseases such as Zika and Ebola, high-prevalence infections such as dengue and malaria, cancers and genetic diseases.

enVision requires 30 minutes to one hour to detect the diseases, which is considered two to four times faster than existing infection diagnostics methods.

Furthermore, each test kit is said to cost less than S$1, which is almost 100 times lower than the current cost for similar tests.

The test delivers results by a colour change and these results can be further analysed by a smartphone app for quantitative assessment of the pathogen. The researchers believe that this capability makes the diagnostic kit ideal for personal healthcare and telemedicine.

NUS Biomedical Engineering department assistant professor Shao Huilin said: “The enVision platform is extremely sensitive, accurate, fast, and low-cost. It works at room temperature and does not require heaters or special pumps, making it very portable.

With this invention, tests can be done at the point-of-care, for instance in community clinics or hospital wards, so that disease monitoring or treatment can be administered in a timely manner to achieve better health outcomes.”

When tested with a clinical model of the human papillomavirus (HPV), enVision is said to have shown better sensitivity and specificity than clinical gold standard.

The researchers now intend to develop a sample preparation module intended to extract and treat DNA material. This is set to be integrated with the diagnostic kit to improve point-of-care application.

Furthermore, the team believes that the smartphone app can have more advanced image correction and analysis algorithms in order to enhance its performance for real-world application.