Genome editing has advanced at a rapid pace with promising results for treating genetic conditions—but there is always room for improvement. A new paper by investigators from Mass General Brigham published in Nature showcases the power of scalable protein engineering combined with machine learning to boost progress in the field of gene and cell therapy. In their study, authors developed a machine learning algorithm—known as PAMmla—that can predict the properties of about 64 million genome editing enzymes. The work could help reduce off-target effects and improve editing safety, enhance editing efficiency, and enable researchers to predict customized enzymes for new therapeutic targets.  Their results are published in Nature.

Findings included elevated risks for chronic organ disease among children, and revealed some racial differences in long COVID risks

Dopamine is the brain’s motivational spark, driving us to chase what feels good, say scrolling another reel on social media, and steer clear of what doesn’t, like touching a hot stove. But scientists haven’t fully understood how dopamine helps us learn to avoid bad outcomes — until now.

A new study from Northwestern University shows that dopamine signals in two key brain areas involved in motivation and learning respond differently to negative experiences, helping the brain adapt based on whether a situation is predictable or controllable. 

While previous research has shown that dopamine can respond to negative experiences, this is the first study to track how those signals evolve over time as animals move from novices to experts in avoiding them.