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Disease-Modifying, Neuroprotective Effect of N-Acetyl-l-Leucine in Adult and Pediatric Patients With Niemann-Pick Disease Type C

Background and ObjectivesN-acetyl-l-leucine (NALL) has been established to improve the neurologic manifestations of Niemann-Pick disease type C (NPC) after 12 weeks in a placebo-controlled trial. In the open-label extension phase (EP) follow-up, data were…

Outcomes in Transplant Recipients With Advanced Cancers Receiving Immune Checkpoint Inhibitors

This systematic review and individual participant data met-analysis assesses the cancer-related and survival outcomes in solid organ transplant recipients with advanced-stage cancer treated with immune checkpoint inhibitors.

Researchers discover link between key protein and brain synapse development

Scientists have uncovered how a protein helps build and maintain vital brain connections, providing insights into the neurological problems experienced by people with a rare form of muscular dystrophy known as dystroglycanopathy.

The research conducted at Oregon Health & Science University and published in Communications Biology reveals that the protein Dystroglycan plays a critical role in forming and maintaining connections between nerve cells in the cerebellum—the part of the brain responsible for movement coordination and motor learning.

In people with dystroglycanopathy, in the protein affect not only muscles but also the brain. The condition is a type of congenital muscular dystrophy, a group of inherited disorders that appear at birth or in early infancy.

AI-powered ChronoFlow uses stellar rotation rates to estimate stars’ ages

Figuring out the ages of stars is fundamental to understanding many areas of astronomy—yet, it remains a challenge since stellar ages can’t be ascertained through observation alone. So, astronomers at the University of Toronto have turned to artificial intelligence for help.

Their new , called ChronoFlow, uses a dataset of rotating stars in clusters and machine learning to determine how the speed at which a star rotates changes as it ages.

The approach, published recently in The Astrophysical Journal, predicts the ages of stars with an accuracy previously impossible to achieve with analytical models.