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Sugar-Coated Nanoparticles Shrink Deadly Brain Tumors in Mice

Cancers don’t come much worse than the brain cancer glioblastoma, and it is notoriously difficult to treat. Even with surgery, radiation, and chemotherapy, fewer than 30 percent of patients are alive two years after diagnosis.

Scientists are busy hunting for treatment approaches that can improve those survival rates, and a team from Oregon State University has now found a potential new angle for attacking these tumors: sugar-coated nanoparticles.

As detailed in a mouse study published in the Journal of Controlled Release, the sugar ‘disguise’ used by the nanoparticles helps them cross the blood-brain barrier to the site of cancer, while also directly targeting glioblastoma and avoiding measurable toxicity in major organs.

Epidurals not linked to increased harm for newborns or children

Having an epidural during labor is not associated with clinically significant increased risks of harm to newborn babies, including brain injury, severe breathing problems, sepsis and death, or cerebral palsy later in childhood, according to a study published in The BMJ.

The researchers say these findings “support widening availability and equitable access to epidural analgesia as a safe component of intrapartum care.”

Epidural analgesia in labor provides effective pain relief and may help reduce complications in mothers after giving birth, but evidence of its effect on newborn and child health is limited.

The invisible wearable: New skin sensors advance health monitoring

While wearable health sensors are becoming increasingly common, current iterations are awkward to wear. For example, devices attached to the face can draw unwanted attention, increase self-consciousness and influence the signals users are trying to measure. However, recent research may have found a solution by introducing ultrathin sensors that cannot be seen by observers or felt by the wearer.

In an article published in Science Advances, researchers from the Institute of Industrial Science, The University of Tokyo, and collaborating institutions reported developing thin, stretchable on-skin electrodes that are effectively invisible when worn on the face. The new technology can measure biological signals while remaining undetectable by eye and touch, allowing monitoring to take place under more natural conditions.

Biosignals such as eye movements, facial muscle activity and brain activity provide valuable information for health care monitoring and human-machine interaction. However, conventional facial electrodes can alter a person’s appearance and affect social interactions, creating what are called appearance artifacts—changes in behavior or psychological state caused simply by wearing a device that the individual and others can see.

Effector ORFeome platform identifies novel pathways

To prevent viruses from sickening or killing us — whether it’s an individual case of hepatitis B or a COVID pandemic — it’s crucial to understand how the proteins they make initiate changes in our bodies that allow them to flourish.

A new tool has just vastly broadened the scale at which researchers can study these proteins, promising to speed basic discoveries in virology, inform the development of new vaccines and treatments, and help humanity protect against emerging pathogens.

The tool, called a viral ORFeome and described in Cell, is the largest yet of its kind and enables analysis of many thousands of viral proteins in a single experiment. Its design also expands access to biologists who didn’t train in virology.

Semaglutide May Slow the Pace of Epigenetic Aging

While GLP-1 drugs can curb cardiometabolic dysfunction, reducing the risk of life-limiting and life-threatening diseases that would otherwise shorten health and lifespan, mechanistic evidence that they directly influence the biology of aging remains limited.

Can GLP-1 drugs rewind the epigenetic clock?

UCSD researchers conducted a post hoc analysis of a Phase 2b clinical trial evaluating the use of semaglutide in human immunodeficiency virus (HIV)-associated lipohypertrophy (HALS).

Joint trajectories of brain atrophy, white matter hyperintensities and cognition quantify brain maintenance

Joint longitudinal modelling of brain atrophy, white matter damage, and cognition in 543 older adults yielded a brain maintenance index. Poorer mental health, lower openness, and faster biological ageing predicted reduced maintenance.

Host Cell Virus Interactions: Molecular Mechanisms, Immune Modulation, Viral Pathogenesis, and Emerging Therapeutic Targets

Host–virus relationships regulate every phase of viral infection and critically influence course of illness and the effectiveness of treatment. Viruses utilize host receptors, intracellular trafficking routes, metabolic programs, and immunological signaling networks to introduce infection, while host cells use innate and adaptive immune responses that both limit viral replication and, in certain situations, cause tissue damage. Given the fast viral evolution and drug resistance linked to virus-directed therapy, there is growing proof that these host-dependent mechanisms are appealing and underutilized targets for antiviral treatment.

With machine learning, researchers embrace the atomic-scale complexity of batteries

For grid-scale energy storage and national energy resilience, the U.S. needs better batteries. Lawrence Livermore National Laboratory (LLNL) scientists are tackling that challenge in many ways, but one approach is making a significant impact: physics-informed machine learning.

In two recent publications, LLNL researchers examined how integrating molecular dynamics simulations with physics-informed machine learning can illuminate the relationships between structure and behavior in complex battery materials. They used the combination of techniques to explore carbon anodes in sodium-ion batteries and liquid electrolytes in lithium-ion batteries.

“These studies show that the structural complexity of battery materials is not just an obstacle to understanding but a design advantage, laying the groundwork for high-throughput screening of next-generation energy-storage materials,” said LLNL scientist and author Liwen (Sabrina) Wan. “By encoding that complexity into physics-informed machine learning models, we can predict properties and identify design levers that traditional approaches simply cannot access.”

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