The National Institutes of Health awarded $248.7 million to seven companies to accelerate the development of new coronavirus diagnostic technologies as the country grapples with rising case numbers and testing shortages. The federal research agency expects some of the companies to potentially be able to perform hundreds of thousands of tests come September. Read more at Forbes.

On Friday, the NIH announced the first seven winners of a competition to produce next-generation coronavirus tests to help battle the spread of COVID-19. Together, they will receive $248.7 million to further develop their tests and hopefully make them available by the fall. Read more at NPR.

he National Institutes of Health selected Ginkgo Bioworks, Mammoth Biosciences, Quidel, and four other companies to receive nearly $250 million to develop new Covid-19 diagnostic tests through its Rapid Acceleration of Diagnostics program, the agency announced Friday. Read more at STAT.

NIH is investing $248.7 million in new technologies to address challenges associated with COVID-19 testing (which detects SARS-CoV-2 coronavirus). NIH’s RADx initiative has awarded contracts to seven biomedical diagnostic companies to support a range of new lab-based and point-of-care tests.

Former NIBIB Director calls for collaboration among engineers and health professionals to address complicated challenges, including COVID19.

In a paper in the New England Journal of Medicine, scientific leaders from the National Institutes of Health set forth a framework to increase significantly the number, quality and type of daily tests for detecting SARS-CoV-2, the virus that causes COVID-19, and help reduce inequities for underserved populations that have been disproportionally affected by the disease.

Scientists at NIBIB have developed new image processing techniques for microscopes that can reduce post-processing time up to several thousand-fold.

A new technique funded by NIBIB and developed by University of Minnesota researchers allows 3D printing of hydrogel-based sensors directly on the surface of organs, such as lungs—even as they expand and contract. The technology was developed to support robot-assisted medical treatments.

NIBIB-funded researchers at Stanford University have created an artificial neural network that analyzes lung CT scans to provide information about lung cancer severity that can guide treatment options.