You’ve likely met someone who identifies as a visual or auditory learner, but others absorb knowledge through a different modality: touch. Being able to understand tactile interactions is especially important for tasks such as learning delicate surgeries and playing musical instruments, but unlike video and audio, touch is difficult to record and transfer.

Using a machine-learning algorithm, researchers can predict interactions that could interfere with a drug’s effectiveness.

The first documented case of pancreatic cancer dates back to the 18th century. Since then, researchers have undertaken a protracted and challenging odyssey to understand the elusive and deadly disease. To date, there is no better cancer treatment than early intervention. Unfortunately, the pancreas, nestled deep within the abdomen, is particularly elusive for early detection. 

These compounds can kill methicillin-resistant Staphylococcus aureus (MRSA), a bacterium that causes deadly infections.

The machine-learning method works on most mobile devices and could be expanded to assess other motor disorders outside of the doctor’s office.

The findings could help doctors identify cancer patients who would benefit the most from drugs called checkpoint blockade inhibitors.

The device contains encapsulated cells that produce insulin, plus a tiny oxygen-producing factory that keeps the cells healthy.

The new approach could lead to intranasal vaccines for Covid-19 and other respiratory diseases.

A potential new Alzheimer’s drug represses the harmful inflammatory response of the brain’s immune cells, reducing disease pathology, preserving neurons, and improving cognition in preclinical tests.