Aqueous zinc (Zn) metal batteries (AZMBs) have distinct advantages in terms of safety and cost-effectiveness. However, the industrial application of AZMBs is currently not ready due to challenges of Zn dendrite growth and the side reactions such as hydrogen evolution reaction (HER) on the Zn anodes. In this review, we discuss how inorganic interfaces impact the Zn²⁺ plating/stripping reaction and overall cell performance. The discussion is categorized based on the types of inorganic materials, including metal oxides, other metal compounds, and inorganic salts. The proposed protection mechanisms for Zn metal anodes are highlighted, with a focus on the dendrite and HER inhibition mechanisms facilitated by various inorganic materials. We also provide our perspective on the rational design of advanced interfaces to enable highly reversible Zn²⁺ plating/stripping reactions toward highly stable AZMBs, paving the way for their practical implementation in energy storage.

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.

Researchers from Tianjin University have introduced the Emergency Medical Procedures 3D Dataset (EMP3D), a pioneering resource that captures the intricate movements of medical professionals during life-saving interventions with unprecedented precision. Published on 15 November 2025 in Frontiers of Computer Science co-published by Higher Education Press and Springer Nature, this dataset leverages synchronized multi-camera systems, advanced AI algorithms, and rigorous human validation to create the first 3D digital blueprint of emergency medical workflows. The innovation holds the potential to fundamentally transform emergency medical training and enhance robotic support in healthcare settings.

Multimorbidity is defined as the concurrent presence of two or more age-associated diseases within an individual, which often results in detrimental health outcomes. Immunosenescence, the gradual deterioration of the immune system associated with aging, constitutes a significant risk factor for the development of these conditions. Moreover, certain diseases may exacerbate immunosenescence, thereby establishing a self-perpetuating pathological cycle. This bidirectional interaction forms a complex pathological network that presents considerable challenges for both the investigation and prevention of multimorbidity. In light of these challenges, it is pertinent to consider whether a paradigm shift in research and intervention strategies—centered on protective factors or anti-aging mechanisms—could facilitate substantial advancements in this field. On October 13, 2025, a research team led by Associate Researcher Xiaolin Ni and Researcher Erping Long from the Institute of Basic Medical Sciences at the Chinese Academy of Medical Sciences published an article entitled "Centenarians: a model of immune resilience against multimorbidity" in the journal Life Medicine. By examining the mechanistic interplay between immunosenescence and multimorbidity and referencing the unique immune profiles of centenarians, the authors proposed a novel systemic therapeutic paradigm termed IMET. This approach focuses on the modulation and restoration of the immune microenvironment, thereby offering innovative research directions and intervention strategies aimed at mitigating the burden of aging-related multimorbidity.