Type 2 diabetes (T2D) and obesity are metabolic conditions with many causes, including overlapping and distinct genetic features. A polygenic risk score (PRS) can capture multiple genetic risk factors to provide an estimate for whether a person may develop a complex medical condition and how they might fare long-term.

Building stronger genetic risk scores
By integrating genetic findings from several of the world’s largest biobanks, investigators from Mass General Brigham built metabolic PRSs for predicting obesity and T2D, which outperformed existing disease-prediction models and predicted downstream morbidity and clinical interventions. Findings are published in Cell Metabolism.

“Our intention was to not only capture the risk of being diagnosed ...

What if electronic circuits could be created simply by drawing lines with a pencil on paper or leaves—and then immediately applied to soft robots or skin-attached health monitoring devices? Korean researchers have developed an electronic materials technology that forms electrically conductive liquid metal in a fine powder form, allowing circuits to be drawn directly on a wide variety of surfaces.

This technology presents new possibilities for next-generation flexible electronics, including applications on paper and plastic as well as in soft robotic systems and wearable devices. The research was published in Advanced Functional Materials.

A research team led by Distinguished Professor Inkyu Park from the Departm...

The Small Magellanic Cloud (SMC) is one of the Milky Way’s closest galactic neighbors—a small, gas-rich galaxy visible to the naked eye from the southern hemisphere, and bound to our galaxy by gravity, alongside its companion, the Large Magellanic Cloud (1¹LMC). All three galaxies have been interacting for hundreds of millions of years.

The SMC is also one of the most studied galaxies in the sky. Astronomers have catalogued its stars, mapped its gas and tracked its motion for more than half a century. Yet a basic question about it has remained...

A research team led by Lee Hyun Jun and Noh Hee Yeon from the Division of Nanotechnology at DGIST has succeeded in implementing the world’s first two-terminal-based artificial intelligence (AI) semiconductor that precisely controls hydrogen with electrical signals to enable self-learning and memory. The team’s work appears in Advanced Science.

Whereas modern AI requires the rapid processing of vast amounts of data, the separation of computation and memory in conventional computers results in speed degradation and high power consumption...