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Nanoparticle blueprints reveal path to smarter medicines

Lipid nanoparticles (LNPs) are the delivery vehicles of modern medicine, carrying cancer drugs, gene therapies and vaccines into cells. Until recently, many scientists assumed that all LNPs followed more or less the same blueprint, like a fleet of trucks built from the same design.

Now, in Nature Biotechnology, researchers from the University of Pennsylvania, Brookhaven National Laboratory and Waters Corporation have characterized the shape and structure of LNPs in unprecedented detail, revealing that the particles come in a surprising variety of configurations.

That variety isn’t just cosmetic: As the researchers found, a particle’s internal shape and structure correlates with how well it delivers therapeutic cargo to a particular destination.

‘Messy’ galaxies in the early universe struggled to settle, Webb reveals

Astronomers using the James Webb Space Telescope (JWST) have captured the most detailed look yet at how galaxies formed just a few hundred million years after the Big Bang—and found they were far more chaotic and messy than those we see today.

The team, led by researchers at the University of Cambridge, analyzed more than 250 young galaxies that existed when the universe was between 800 million and 1.5 billion years old. By studying the movement of gas within these galaxies, the researchers discovered that most were turbulent, “clumpy” systems that had not yet settled into smooth rotating disks like our own Milky Way.

Their findings, published in Monthly Notices of the Royal Astronomical Society, suggest that galaxies gradually became calmer and more ordered as the universe evolved. But in the , star formation and gravitational instabilities stirred up so much turbulence that many galaxies struggled to settle.

Algorithm maps genetic connection between Alzheimer’s and specific neurons

The number of people living with dementia worldwide was estimated at 57 million in 2021 with nearly 10 million new cases recorded each year. In the U.S., dementia impacts more than 6 million lives, and the number of new cases is expected to double over the next few decades, according to a 2025 study. Despite advancements in the field, a full understanding of disease-causing mechanisms is still lacking.

To address this gap, Rice University researchers and collaborators at Boston University have developed a that can help identify which specific types of cells in the body are genetically linked to complex human traits and diseases, including in forms of dementia such as Alzheimer’s and Parkinson’s.

Known as “Single-cell Expression Integration System for Mapping genetically implicated Cell types,” or seismic, the tool helped the team hone in on genetic vulnerabilities in memory-making brain cells that link them to Alzheimer’s ⎯ the first to establish an association based on a genetic link between the disease and these specific neurons. The algorithm outperforms existing tools for identifying that are potentially relevant in complex diseases and is applicable in disease contexts beyond dementia.

Geochemical research could help identify microbial activity in Earth’s rock record and perhaps in Martian sediments

Because oxygen-bearing sulfate minerals trap and preserve signals from Earth’s atmosphere, scientists closely study how they form. Sulfates are stable over billions of years, so their oxygen isotopes are seen as a time capsule, reflecting atmospheric conditions while they were evolving on early Earth—and possibly on its planetary neighbor Mars.

A new study led by a University of Utah geochemist examines how forms when pyrite, commonly known as “fool’s gold,” is oxidized in environments teeming with microbes versus those without them. The researchers focused on Spain’s Rio Tinto, a contaminated river passing through a region where iron and copper were mined for thousands of years.

The paper titled, “Triple-oxygen isotopic evidence of prolonged direct bioleaching of pyrite with O2,” appears in Earth and Planetary Science Letters.

Quantum Systems Modeled Without Prior Assumptions

An improved algorithm for learning the static and dynamic properties of a quantum system could have applications in quantum computing, simulation, and sensing.

Quantum systems are notoriously hard to study, control, and simulate. One key reason is that their full characterization requires a vast amount of information. Fortunately, in the past decade, scientists have shown that many physical properties of a quantum system can be efficiently predicted using much less information [1, 2]. Moreover, researchers have built quantum sensors that can measure these properties with a much smaller uncertainty compared with the best classical sensors [3]. Nevertheless, it has been difficult to achieve both efficient predictions and precise measurements at the same time. Now, building on previous breakthroughs in the field, Hong-Ye Hu at Harvard University and his colleagues have demonstrated a new algorithm that characterizes quantum systems of any size with optimal efficiency and precision [4].

Researchers help break thermal conductivity barrier with boron arsenide discovery

University of Houston researchers have made a discovery in thermal conductivity that overturns an existing theory that boron arsenide (BAs) couldn’t compete with the heat conduction of a diamond.

Instead, the team found that high-quality crystals can achieve exceeding 2,100 watts per meter per Kelvin (W/mK) at room temperature—possibly higher than diamond, which has been considered the best heat conductor among isotropic materials.

Published in Materials Today, this research challenges existing theories and could reshape our understanding of heat-conducting materials. It could also bring forth a new semiconductor material with much better thermal management in cell phones, high-powered electronics and .

Exploring how dark matter alters electron-capture supernovae and the birth of neutron stars

Electron-capture supernovae (ECSNe) are stellar explosions that occur in stars with initial masses around 8–10 times that of the sun. These stars develop oxygen-neon-magnesium cores, which become unstable when electrons are captured by neon and magnesium nuclei.

A tiny chip that can help us see deeper into space

A new imaging system could help us see deeper into the universe than ever before. The same powerful technology could also help us analyze the chemical makeup of everyday materials such as food and medicines much faster and with greater accuracy than current methods.

In a study published in the journal Nature, researchers from Tsinghua University in China have introduced a tiny device called RAFAEL (Reconfigurable, Adaptive, FAst and Efficient Lithium-niobate spectro-imager) that uses advanced photonics to capture light in exceptional detail with high speed.

RAFAEL is designed to dramatically improve spectroscopy, the technique used to study the and chemical composition of matter. It is used for everything from mapping to checking for contaminants in water and diagnosing diseases, and it works by breaking down the light that comes from an object and analyzing the different colors (wavelengths). While incredibly powerful, traditional spectrometers often face a trade-off: To get very fine detail you have to block much of the light. Or if you let in a lot of light, you lose resolution or sensitivity.

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