Single-atom nanozymes (SAzymes) exhibit exceptional catalytic efficiency due to their maximized atom utilization and precisely modulated metal-carrier interactions, which have attracted significant attention in the biomedical field. However, stability issues may impede the clinical translation of SAzymes. This review provides a comprehensive overview of the applications of SAzymes in various biomedical fields, including disease diagnosis (e.g., biosensors and diagnostic imaging), antitumor therapy (e.g., photothermal therapy, photodynamic therapy, sonodynamic therapy, and immunotherapy), antimicrobial therapy, and anti-oxidative stress therapy. More importantly, the existing challenges of SAzymes are discussed, such as metal atom clustering and active site loss, ligand bond breakage at high temperature, insufficient environment tolerance, biosecurity risks, and limited catalytic long-term stability. Finally, several innovative strategies to address these stability concerns are proposed—synthesis process optimization (space-limited strategy, coordination site design, bimetallic synergistic strategy, defect engineering strategy, atom stripping-capture), surface modification, and dynamic responsive design—that collectively pave the way for robust, clinically viable SAzymes.

Researchers have developed a novel generative AI model, called Collaborative Competitive Agents (CCA), that significantly improves the ability to handle complex image editing tasks. This new approach utilizes multiple Large Language Model (LLM)-based agents that work both collaboratively and competitively, resulting in a more robust and accurate editing process compared to existing methods. This breakthrough allows for a more transparent and iterative approach to image manipulation, enabling a level of precision previously unattainable. The findings were published on 15 November 2025 in Frontiers of Computer Science, co-published by Higher Education Press and Springer Nature.

Cognitive Diagnosis (CD) plays a crucial role in personalized learning by evaluating students' mastery of various concepts. However, current CD models face a significant challenge—the "student-concept sparsity barrier." This occurs when students have limited interactions with certain concepts.

The privacy protection issue of multi-domain traffic engineering has always been a hot topic. Consider that domains from different service providers may not be willing to expose the topology information within their own domains for security and privacy reasons.

Multilingual Large Language Models (MLLMs) have achieved remarkable success in advancing multilingual natural language processing, enabling effective knowledge transfer from high-resource to low-resource languages. Despite their achievements, MLLMs still face numerous issues and challenges, which can be categorized into three main aspects: corpora, alignment, and bias.

The 2024 Nobel Prize in Chemistry was recently granted to David Baker, Demis Hassabis and John M. Jumper, renowned for their pioneering works in protein design.

Network security has always been the focus of Internet research. In order to better detect these attacks, it is necessary to more accurately evaluate the state performance of the network. The existing measurement methods often only focus on a single or very few indicators.