A new strategy for immune tolerance
Peer-Reviewed Publication
Updates every hour. Last Updated: 10-Jun-2026 15:16 ET (10-Jun-2026 19:16 GMT/UTC)
A research team at the Nano Life Science Institute (WPI-NanoLSI) and the Faculty of Medicine at Kanazawa University has developed a new class of engineered extracellular vesicles (EVs) capable of inducing antigen-specific regulatory T cells (Tregs), the immune cells that play a central role in suppressing excessive immune responses. The findings, now published in Drug Delivery, may pave the way for next-generation therapies for autoimmune and allergic diseases, where unwanted immune activation must be precisely controlled.
Abstract:
A research group led by Professor Hiroaki SUZUKI and Takeshi HAYAKAWA from the Faculty of Science and Engineering at Chuo University, graduate student Zhitai HUANG, graduate students Kanji KANEKO (at the time) and Ryotaro YONEYAMA (at the time), together with Specially Appointed Assistant Professor Tomoya MARUYAMA from the Research Center for Autonomous Systems Materialogy (ASMat), Institute of Integrated Research (IIR), Institute of Science Tokyo, and Professor Masahiro TAKINOUE from the Laboratory for Chemistry and Life Science, Institute of Integrated Research, Institute of Science Tokyo, has developed a novel and highly accessible technology for producing uniform Biomolecular Condensates*1) using a simple, low-cost vibration platform.
In the era of global space industry's rapid expansion, reusable launch technology addresses cost reduction, but achieving high launch cadence and flight reliability remains critical. This study published in the Chinese Journal of Aeronautics (Volume 38, Issue 10, October 2025, https://doi.org/10.1016/j.cja.2025.103756), proposes that artificial intelligence (AI) would be the potential disruptive technology to solve these challenges. AI enables transformative capabilities for launch vehicles are pointed out in four domains: Agile launch operations enabling automate testing, fault diagnosis, and decision-making for targeting hour-level launch cycles and minute-level fault resolution; High-reliability flight enabling real-time autonomous fault diagnosis, mission replanning, and fault-tolerant control within seconds during anomalies, potentially improving reliability by 1-2 orders of magnitude; Rapid maintenance enabling real-time health monitoring and lifespan prediction for swift re-launch decisions; and Efficient space traffic management enabling predict/resolve orbital conflicts amid growing congestion from satellites and debris. The key challenges for AI applications are analyzed as well, including multi-system coupling, uncertain failure modes and narrow flight corridors, limited sensor data, and massive heterogeneous data processing. Finally, the study also proposes that AI promises substantial efficiency gains in launch vehicle design, manufacturing, and testing through multidisciplinary optimization and reduced reliance on physical testing.
A new study introduces ACA-SIM (atmospheric correction based on satellite–in situ matchup data), a neural-network-based atmospheric correction algorithm that uses real satellite–Aerosol Robotic Network-Ocean Color (AERONET-OC) matchups to improve the accuracy of atmospheric correction over coastal waters.
A Chinese Medical Journal review details innovative approaches to overcome solid tumor microenvironment barriers for CAR-T therapy, including vascular normalization, chemokine engineering, physical barrier targeting, and novel delivery methods, alongside key clinical challenges.
Researchers have developed a new way to compress the memory used by AI models to increase their accuracy in complex tasks or help save significant amounts of energy.
Experts from University of Edinburgh and NVIDIA found that large language models (LLMs) using memory eight times smaller than an uncompressed LLM scored better on maths, science and coding tests while spending the same amount of time reasoning.