Brain cells that produce the protective coating around nerve fibres can become dysfunctional with age and may actively contribute to cognitive decline, according to new research.

Electrochemical energy technologies are central to the net-zero transition, yet their multiscale characteristics ranging from atomic materials to device architectures present critical challenges in research and development (R&D). Although artificial intelligence (AI) has accelerated discovery and design in this field, commonly used predictive AI methods remain limited in enabling disruptive advances. Generative AI has shown transformative potential in disciplines such as biology and medicine, though its impact on electrochemical energy R&D is only beginning to emerge. Here we review recent progress in applying generative AI to molecular and crystal discovery, electrode microstructure design, and system optimization, with particular attention to the role of large language models in electrochemical engineering. We argue for a paradigm shift toward generative electrochemical intelligence (GenE), a physics-informed, multimodal framework that integrates human expertise with automated experimentation. We anticipate that GenE will redefine the R&D paradigm for rapidly deployable electrochemical energy technologies and accelerate their translation into real-world applications.

A multi-institutional research team from Fuzhou University, Huazhong University of Science and Technology, and collaborating institutions has published a comprehensive review on small-scale magnetic soft robots for medical applications. These miniature, soft robotic systems can be remotely actuated or manipulated by external magnetic fields, allowing operation in confined internal environments where conventional medical instruments may be difficult to miniaturize or maneuver. The review focuses on two major categories, untethered and tethered magnetic soft robots, and compares their development across five translational stages: basic performance testing, artificial model validation, ex vivo organ experiments, in vivo animal studies, and clinical translation. By examining technological progress, key milestones, clinical potential, and remaining barriers, the article provides a systematic roadmap for advancing magnetic soft robots from laboratory research toward safer and more standardized clinical use.

For the first time in Finland, population-level norm values have been established for estimating life expectancy adjusted for health-related functional capacity and productivity. The new study provides Finnish norm values for both everyday functioning and work ability, as well as for a newly developed measure, productivity-adjusted life expectancy.