The review suggests that patient-reported “taste changes” during GLP-1RA treatment may reflect reduced food enjoyment and motivation rather than consistent impairment of basic taste. The authors propose a sensory-liking-wanting framework while emphasizing that current human evidence remains heterogeneous and largely indirect.
Some physical injuries and neurological conditions can temporarily or permanently impair movement, leaving some people unable to speak, type on keyboards or use electronic devices. Brain-computer interfaces (BCIs), systems that can decode brain activity patterns and convert them into computer commands or written text, could be of great value for paralyzed patients.
Despite their potential, most of the best-performing BCIs developed to date require patients to undergo invasive surgical procedures. These systems typically rely on small sensors that need to be implanted on or within the brain and can detect electrical signals associated with neural activity.
Researchers at Meta artificial intelligence (AI), Université PSL and Hospital Foundation Adolphe de Rothschild recently introduced a noninvasive brain activity-to-text approach that does not require surgical procedures. Their proposed approach, presented in Nature Neuroscience, combines a new deep learning algorithm with electroencephalography (EEG) or magnetoencephalography (MEG) recordings.
Researchers at the George W. Woodruff School of Mechanical Engineering have developed a new approach to nanoscale 3D printing that improves both speed and fidelity, overcoming a challenge that has limited the technology’s broader use in manufacturing.
Their work, led by Sourabh Saha, associate professor and Woodruff Faculty Fellow, and Harnjoo Kim, who conducted the research during his doctoral studies and later as a postdoctoral fellow in Saha’s lab, was recently published in the journal Nature Communications.
Nanoscale 3D printing allows researchers to create structures thousands of times smaller than the width of a human hair. These structures have potential applications in fields ranging from advanced computing and optics to biomedical devices and clean energy technologies. However, increasing printing speed often comes at the expense of quality.
Ultimately, Sapra says, the end-state vision for AI in biologic drug discovery is what the field calls “de novo” design. For this, the goal is for AI to generate entirely new protein sequences that precisely fit the desired drug properties. This includes designing the structure, predicting safety, how it will behave in the body and how to make it manufacturable.
“The field is making great progress toward a completely AI-generated biologic, designed from scratch all the way to a clinical candidate,” Sapra says. “As we continue to leverage frontier models and fine-tune them with the right datasets, we bring ourselves closer to this reality. I believe it will come. It’s a matter of time.”
Several key elements are needed to reach this point, however. First is richer and more standardized training data across the industry. Second, robust evaluation benchmarks for AI-generated candidates. And third, teams that know how to work at the intersection of machine learning and biology. Of all the prerequisites, however, safety prediction may be the most consequential, and perhaps the least discussed, Sapra says.
Cells must constantly repair damage to their DNA that, if left unfixed, can lead to cell death, cancer, or accelerated ageing. EMBL researchers and their collaborators show that the Golgi complex, an organelle best known as the cell’s sorting and shipping centre, stores DNA-repair proteins and dispatches them to the nucleus when damage occurs. When DNA is damaged, the cell recruits the relevant repair proteins from the Golgi to the nucleus, matched to the type of damage, and clears the proteins it doesn’t need out of the nucleus, sequestering them back at the Golgi. This work reveals a new layer of regulation of DNA repair pathways from outside the nucleus, opening up new opportunities to modulate these pathways and to target related diseases such as cancer.
Stanford Medicine researchers have identified a naturally occurring molecule that may suppress appetite and reduce body weight in a way that resembles semaglutide, the active ingredient in Ozempic. In animal studies, the molecule also appeared to avoid several problems associated with the drug, including nausea, constipation and substantial muscle loss.
The molecule, known as BRP, works through a different but related metabolic pathway and activates a separate group of neurons in the brain. That distinction could make it a more precise tool for controlling appetite and body weight.
Low arginine levels may allow cancer cells and viruses to slip past the immune system by reducing production of a crucial cellular warning protein. In mice, arginine-rich diets led to fewer colon tumors and milder viral infections, suggesting a simple supplement could have powerful therapeutic potential.
