When genetic testing reveals a rare DNA mutation, doctors and patients are frequently left in the dark about what it actually means. Now, researchers at the Icahn School of Medicine at Mount Sinai have developed a powerful new way to determine whether a patient with a mutation is likely to actually develop disease, a concept known in genetics as penetrance. The team set out to solve this problem using artificial intelligence (AI) and routine lab tests like cholesterol, blood counts, and kidney function. Details of the findings were reported in the August 28 online issue of Science. Their new method combines machine learning with electronic health records to offer a more accurate, data-driven view of genetic risk.

Adam Castillejo, Paul Edmonds and Marc Franke live a world apart, yet they share one infinitesimally rare trait: Each of them literally embodies the cure for HIV.

Although HIV can now be managed over a lifetime of antiretroviral therapy, which suppresses the viral load, there is no definitive therapy to entirely rid the body of a virus that affects an estimated 40 million people worldwide — with a tiny number of notable exceptions.

In the coming year, three of them will gather to officially begin a unique scientific collaboration.