This study uses electronic medical record data to compare 48-week viral load outcomes after starting long-acting antiretroviral therapy among people in the US with HIV with or without viremia from January 2021 through September 2024.
Category: biotech/medical – Page 74
A new study highlights a potential role for immune system dysfunction in the onset and progression of Alzheimer’s disease.
A protein identified nearly 40 years ago for its ability to stimulate the production of red blood cells plays a surprising, critical role in dampening the immune system’s response to cancer.
Blocking the activity of the protein turns formerly “cold,” or immune-resistant, liver tumors in mice into “hot” tumors teeming with cancer-fighting immune cells. When combined with an immunotherapy that further activates these immune cells against the cancer, the treatment led to complete regression of existing liver tumors in most mice. Treated animals lived for the duration of the experiment. In contrast, control animals survived only a few weeks.
“This is a fundamental breakthrough in our understanding of how the immune system is turned off and on in cancer,” said the senior author published the work in Science. “I could not be more excited about this discovery, and I hope treatments that target the mechanism we uncovered will quickly move forward to human trials.”
To understand what drives disease progression in tissues, scientists need more than just a snapshot of cells in isolation—they need to see where the cells are, how they interact, and how that spatial organization shifts across disease states. A computational method called MESA (Multiomics and Ecological Spatial Analysis), detailed in a study published in Nature Genetics, is helping researchers study diseased tissues in more meaningful ways.
The work details the results of a collaboration among researchers from MIT, Stanford University, Weill Cornell Medicine, the Ragon Institute of MGH, MIT, and Harvard, and the Broad Institute of MIT and Harvard, and was led by the Stanford team.
MESA brings an ecology-inspired lens to tissue analysis. It offers a pipeline to interpret spatial omics data—the product of cutting-edge technology that captures molecular information along with the location of cells in tissue samples. This data provides a high-resolution map of tissue “neighborhoods,” and MESA helps make sense of the structure of that map.
In a demographically diverse sample of healthy people, Cornell researchers found dramatic changes over the human lifespan in the brain’s “blue spot”—a tiny region involved in cognition and believed to be the first affected by neurodegenerative conditions including Alzheimer’s disease.
Using specialized MRI scans to measure the intensity of neuromelanin, a pigment that gives the locus coeruleus (LC) its blue color, the research team observed an inverted U-shaped curve that peaked in later middle age before dropping off sharply, a finding that helps characterize healthy aging patterns.
Maintaining a stronger blue signal after age 60 was associated with better cognitive performance, according to the study involving 134 participants aged 19 to 86. Because of the participants’ diversity, including about 40% who were non-white, the researchers also discovered higher peaks among Black participants and women, groups known to be more susceptible to Alzheimer’s.
Combinatorial optimization problems (COPs) arise in various fields such as shift scheduling, traffic routing, and drug development. However, they are challenging to solve using traditional computers in a practical timeframe.
Alternatively, annealing processors (APs), which are specialized hardware for solving COPs, have gained significant attention. They are based on the Ising model, in which COP variables are presented as magnetic spins and constraints as interactions between spins. Solutions are obtained by finding the spin state that minimizes the energy of the system.
There are two types of Ising models, the sparsely-coupled model and the fully-coupled model. Sparsely-coupled models offer high scalability by allowing more spins, but require COPs to be transformed to fit the model. Fully-coupled models, on the other hand, allow any COP to be mapped directly without transformation, making them highly desirable.
University of Oregon researchers have uncovered a molecule produced by yeast living on human skin that showed potent antimicrobial properties against a pathogen responsible for a half-million hospitalizations annually in the United States.
It’s a unique approach to tackling the growing problem of antibiotic-resistant bacteria. With the global threat of drug-resistant infections, fungi inhabiting human skin are an untapped resource for identifying new antibiotics, said Caitlin Kowalski, a postdoctoral researcher at the UO who led the study.
Described in a paper published in Current Biology, the common skin fungus Malassezia gobbles up oil and fats on human skin to produce fatty acids that selectively eliminate Staphylococcus aureus. One out of every three people has Staphylococcus aureus harmlessly dwelling in their nose, but the bacteria are a risk factor for serious infections when given the opportunity: open wounds, abrasions and cuts. They’re the primary cause of skin and soft tissue infections known as staph infections.
Plants are susceptible to a wide range of pathogens. For the common potato plant, one such threat is Pectobacterium atrosepticum, a bacterium that causes stems to blacken, tissues to decay, and often leads to plant death, resulting in significant agricultural losses each year.
In 2012, researchers isolated a new virus that infects and kills this bacterium—a bacteriophage named φTE (phiTE). Now, for the first time, scientists have uncovered the atomic structure of φTE, revealing a possible mechanism of infection that may be more complex than previously thought.
The study, published earlier this month in Nature Communications, is the result of a multidisciplinary collaboration between researchers from the Okinawa Institute of Science and Technology (OIST) and the University of Otago. It brings together expertise across several fields, including virology, structural biology, molecular genetics, protein engineering, biochemistry, and biophysics.
Light signature algorithm offers precise insight on viral proteins, brain disease markers and semiconductors
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Researchers at Rice University have developed a new machine learning (ML) algorithm that excels at interpreting the “light signatures” (optical spectra) of molecules, materials and disease biomarkers, potentially enabling faster and more precise medical diagnoses and sample analysis.
“Imagine being able to detect early signs of diseases like Alzheimer’s or COVID-19 just by shining a light on a drop of fluid or a tissue sample,” said Ziyang Wang, an electrical and computer engineering doctoral student at Rice who is a first author on a study published in ACS Nano. “Our work makes this possible by teaching computers how to better ‘read’ the signal of light scattered from tiny molecules.”
Every material or molecule interacts with light in a unique way, producing a distinct pattern, like a fingerprint. Optical spectroscopy, which entails shining a laser on a material to observe how light interacts with it, is widely used in chemistry, materials science and medicine. However, interpreting spectral data can be difficult and time-consuming, especially when differences between samples are subtle. The new algorithm, called Peak-Sensitive Elastic-net Logistic Regression (PSE-LR), is specially designed to analyze light-based data.
Researchers at the University of Pittsburgh have created a groundbreaking tissue engineering platform using 3D-printed collagen scaffolds called CHIPS.
By mimicking natural cellular environments, they enable cells to grow, interact, and form functional tissues — a major step beyond traditional silicone-based microfluidic models. The platform not only models diseases like diabetes but could also replace animal testing in the future. Plus, their designs are freely available to fuel broader scientific innovation.
3D bioprinting: turning science fiction into science reality.