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AI-designed universal vaccine clears first human trial, targets future coronavirus threats with needle-free delivery

The first human clinical trial of a universal Sarbeco coronavirus vaccine, developed by the University of Cambridge and spin-out DIOSynVax (DVX) Ltd, has shown that the vaccine is safe and has no significant side effects.

The trial, involving 39 healthy volunteers, tested a vaccine designed to provide protection against multiple Sarbeco coronaviruses—the large group of viruses that occur in nature including SARS-CoV-2, which caused the COVID pandemic.

The vaccine triggered immune responses in the volunteers not only to SARS-CoV-2 and SARS, but to related bat viruses that could potentially jump from animals to humans and cause future pandemics.

Faster aptamer screening finds synthetic alternatives to antibodies in days instead of months

Aptamers are short DNA or RNA strands that can recognize and bind to a specific target molecule with high precision. Similar to antibodies, they can be used to detect these molecules or modulate their activity. Unlike antibodies, they are much more stable, can be produced synthetically and can be chemically modified to achieve the desired properties. As a result, they can offer capabilities that cannot be achieved with antibodies.

As demand grows for accurate and rapid diagnostic tools, aptamers are often better suited to these applications than antibodies. However, developing aptamers is both experimentally demanding and time-consuming. A team of scientists from IOCB Prague, led by Dr. Marek Ondruš and Prof. Michal Hocek, has now developed a technology that significantly shortens the development process. Their research is published in the journal Nature Communications.

Specific cognitive abilities are highly heritable independent of general intelligence

A massive new meta-analysis reveals that individual cognitive abilities, like reading and math, rely on inherited DNA just as much as overall intelligence, suggesting people possess heavily customized genetic cognitive profiles independent of general smarts.

Replacement‐Based Ageing Interventions for Systemic Rejuvenation: Shaping Longevity Science and Clinical Directions

We propose a roadmap to guide research and innovation integrating replacement and next-generation damage-removal therapeutics to modulate the ageing process in the whole body, restore biological function, and extend healthy lifespan.

Scientists develop wearable robotic system to restore hand function

Researchers at the Medical University of Vienna, in collaboration with ETH Zurich, the Technical University of Munich and Medical Faculty Belgrade, have developed a wearable neurorobotic system that combines electrical neurostimulation with hand exoskeletons. In a clinical trial involving 14 patients with hand impairments caused by neurological injury, the technology supported finger mobility, tactile perception and grip control. The results demonstrate the potential of personalised assistive systems for people living with the consequences of spinal cord or brain injury. The study has recently been published in the journal Science Advances.

Hand movements and the sense of touch are essential for everyday activities such as grasping, eating, dressing or personal hygiene. However, after damage to the central nervous system, motor and sensory impairments of the hand often persist. Conventional rehabilitation can achieve improvements, but does not always lead to sufficient restoration of hand function. There is therefore a great need for assistive technologies suitable for everyday use.

A research team led by study director Stanisa Raspopovic from the Center for Medical Physics and Biomedical Engineering at MedUni Vienna has developed the “SensoExo” system for assisting people with hand sensorimotor impairements. It combines a wearable hand exoskeleton with a custom-fitted neurostimulation sleeve. The sleeve stimulates specific nerves and muscles in the forearm through the skin. Sensors on the fingers detect touch and gripping forces and translate this information into electrical stimulation, providing users with tactile feedback. In addition, functional electrical stimulation can assist users open and close their fingers more easily.

New AI math tool could sharpen image editing, drug discovery and simulations

Clarkson University researchers have developed a new mathematical tool that could make artificial intelligence systems more accurate, controllable and useful across applications ranging from image editing to drug discovery.

Clarkson University postdoctoral researcher Zander Blasingame and Chen Liu, professor of electrical and computer engineering, created a new family of numerical solvers called Rex that improves how generative AI models move between random noise and meaningful data. Their work, “Rex: A Family of Reversible Exponential (Stochastic) Runge-Kutta Solvers,” will be presented this summer at the International Conference on Machine Learning (ICML 2026), and an earlier version of the paper is available on the arXiv preprint server.

Diffusion and flow-matching models are the foundation of many modern generative AI systems, including image generators, molecular design tools and scientific simulators. They work by gradually transforming random noise into useful outputs. While that process is effective for creating new content, many important applications require running it in reverse. Existing methods often introduce errors that make it difficult to accurately recover the original information.

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