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New multiple sclerosis criteria could reveal disease before symptoms appear

The international guidelines for diagnosing multiple sclerosis (MS), called the McDonald criteria, underwent their most significant overhaul in a decade in 2024. The internationally recognized framework is used to diagnose MS by combining clinical, imaging and laboratory findings. Dr. Jiwon Oh, director of the BARLO MS Centre at St. Michael’s Hospital, was among the experts who helped write them.

Now, in a commentary published in Nature Medicine, Oh has brought together nearly 30 of the world’s leading MS clinicians to critically evaluate what those changes mean and where the field must go next to improve health outcomes for people with MS. Many of its co-authors also helped develop the revised criteria in 2024.

“This commentary looks critically at the new criteria and explains why these changes matter, what challenges may arise as they’re used, and what can be done to address them,” explains Oh. “It also takes a broader look at where the field needs to go next.”

Codon Usage Bias in Human RNA Viruses and Its Impact on Viral Translation, Fitness, and Evolution

Synonymous codon usage (codon bias) greatly influences not only translation but also mRNA stability. In vertebrates, highly expressed genes preferentially use codons with an optimal tRNA adaptation index (tAI) that mostly end in C or G. Surprisingly, the codon usage of viruses infecting humans often deviates from optimality, showing an enrichment in A/U-ending codons, which are generally associated with slow decoding and reduced mRNA stability. This observation is particularly evident in RNA viruses causing respiratory illnesses in humans. This review analyzes the mutational and selective forces that shape nucleotide composition and codon usage drift in human RNA viruses, as well as their impact on translation, viral fitness, and evolution. It also describes how some viruses overcome suboptimal codon usage to outcompete host mRNA for translation.

AI tool improves DNA-DNA predictions

Researchers have demonstrated a novel AI model that can predict which DNA molecules bind with which other DNA molecules. Providing a more thorough understanding of these hypercomplex binding relationships has utility in applications ranging from biomedical diagnostic tools to DNA computing.

“We often think about binding as a very simple relationship – Molecule A binds to Molecule B,” says the co-corresponding author of the study. “But in biological systems, it’s far from simple. Molecule A may bind to dozens of other molecules, to varying degrees.

Capturing that hypercomplexity is a significant challenge, but it is critical if we want to better understand natural genetic systems, says the author. And capturing that hypercomplexity is also critical if we want to develop tools that make full use of biomolecules, such as diagnostic tools that are sensitive to genetic differences or DNA computing systems that rely on DNA to store and retrieve data.

New model improves short- and long-term disease risk prediction

Researchers developed ALADYNOULLI, a Bayesian generative model that combines longitudinal health records, age, and polygenic risk to identify reproducible disease signatures across more than 683,000 participants. In UK Biobank testing, the framework achieved stronger short- and long-term risk discrimination than established clinical scores while revealing disease subgroups and genetic associations.

This sugar-coated therapy boosted survival against deadly brain cancer by 50% in mice

A new experimental treatment may have found a way to outsmart glioblastoma’s toughest defense: the blood-brain barrier. Researchers used sugar-coated nanoparticles to ferry genetic instructions that restore a key tumor-suppressing protein directly into brain cancer cells. In mouse studies, the therapy increased median survival by 50% while shrinking tumors without noticeable damage to other organs.

Listening to ‘ringing’ black holes unlocks future gravitational-wave astronomy

Listening to the “ringing” produced by black holes after they collide and merge could allow scientists to test Einstein’s theory of general relativity under the most extreme conditions in the universe while unlocking the secrets of these mysterious objects.

Leading a major international review with the Institute of Physics, astrophysicists at the University of Birmingham, Johns Hopkins University and Instituto Superior Técnico of Lisbon show how black hole “spectroscopy” is rapidly evolving from a theoretical concept into a powerful experimental science. The work is published in the journal Classical and Quantum Gravity.

During the “ringdown” phase following a collision and merger, a newly formed black hole emits characteristic gravitational-wave vibrations known as “quasinormal modes.” By measuring these frequencies, scientists can determine the black hole’s mass and how fast it is spinning, as well as investigate whether Einstein’s theory is correct.

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