Ankylosing spondylitis (AS) is increasingly recognized as an independent risk factor for premature myocardial infarction (MI), yet the molecular bridge linking chronic axial inflammation to acute coronary events remains poorly mapped. Mining four public microarray cohorts (GSE128470, GSE73754, GSE100927, GSE122897) that profile peripheral blood mononuclear cells from AS patients, MI patients and healthy controls, integrative bioinformatics now delivers a concise pathogenic blueprint. Weighted gene co-expression network analysis identified one AS-related and one MI-related module that significantly overlap; machine-learning (LASSO + SVM-RFE) distilled these to two hub genes—S100A12 and MCEMP1—whose transcript levels rise concordantly across both diseases. ROC curves yield AUCs of 0.92–0.96 for distinguishing AS-MI cases from either disease alone, and a nomogram incorporating age, CRP and the two hubs achieves a net reclassification improvement of 34 %.

The rise of large-scale artificial intelligence (AI) models, such as ChatGPT, DeepSeek, and autonomous vehicle systems, has significantly advanced the boundaries of AI, enabling highly complex tasks in natural language processing, image recognition, and real-time decision-making. However, these models demand immense computational power and are often centralized, relying on cloud-based architectures with inherent limitations in latency, privacy, and energy efficiency. To address these challenges and bring AI closer to real-world applications, such as wearable health monitoring, robotics, and immersive virtual environments, innovative hardware solutions are urgently needed. This work introduces a near-sensor edge computing (NSEC) system, built on a bilayer AlN/Si waveguide platform, to provide real-time, energy-efficient AI capabilities at the edge. Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction, coupled with Si-based thermo-optic Mach–Zehnder interferometers for neural network computations, the system represents a transformative approach to AI hardware design. Demonstrated through multimodal gesture and gait analysis, the NSEC system achieves high classification accuracies of 96.77% for gestures and 98.31% for gaits, ultra-low latency (< 10 ns), and minimal energy consumption (< 0.34 pJ). This groundbreaking system bridges the gap between AI models and real-world applications, enabling efficient, privacy-preserving AI solutions for healthcare, robotics, and next-generation human–machine interfaces, marking a pivotal advancement in edge computing and AI deployment.

Rechargeable zinc-ion batteries have emerged as one of the most promising candidates for large-scale energy storage applications due to their high safety and low cost. However, the use of Zn metal in batteries suffers from many severe issues, including dendrite growth and parasitic reactions, which often lead to short cycle lives. Herein, we propose the construction of functional organic interfacial layers (OIL) on the Zn metal anodes to address these challenges. Through a well-designed organic-assist pre-construction process, a densely packed artificial layer featuring the immobilized zwitterionic molecular brush can be constructed, which can not only efficiently facilitate the smooth Zn plating and stripping, but also introduce a stable environment for battery reactions. Through density functional theory calculations and experimental characterizations, we verify that the immobilized organic propane sulfonate on Zn anodes can significantly lower the energy barrier and increase the kinetics of Zn²⁺ transport. Thus, the Zn metal anode with the functional OIL can significantly improve the cycle life of the symmetric cell to over 3500 h stable operation. When paired with the H₂V₃O₈ cathode, the aqueous Zn-ion full cells can be continuously cycled over 7000 cycles, marking an important milestone for Zn anode development for potential industrial applications.

Solid-state sodium batteries (SSSBs) are emerging as a promising alternative to conventional lithium-ion batteries, owing to their enhanced safety, cost-effectiveness as well as the abundance of sodium resources. However, despite their conceptual advantages, significant performance degradation, mainly associated to the electrode-electrolyte interfaces, has hindered their widespread application. A recent study led by researchers from the Beijing Institute of Technology provides a comprehensive mechanistic understanding of interfacial degradation in NASICON-type electrolyte-based solid-state sodium metal batteries. Their work focuses on Na₃Zr₂Si₂PO₁₂ (NZSP), a widely studied ceramic electrolyte known for its robust thermal stability and competitive ionic conductivity, yet plagued by poor long-term interfacial performance.

Solid-state batteries are widely recognized as the next-generation energy storage devices with high specific energy, high safety, and high environmental adaptability. However, the research and development of solid-state batteries are resource-intensive and time-consuming due to their complex chemical environment, rendering performance prediction arduous and delaying large-scale industrialization. Artificial intelligence serves as an accelerator for solid-state battery development by enabling efficient material screening and performance prediction. This review will systematically examine how the latest progress in using machine learning (ML) algorithms can be used to mine extensive material databases and accelerate the discovery of high-performance cathode, anode, and electrolyte materials suitable for solid-state batteries. Furthermore, the use of ML technology to accurately estimate and predict key performance indicators in the solid-state battery management system will be discussed, among which are state of charge, state of health, remaining useful life, and battery capacity. Finally, we will summarize the main challenges encountered in the current research, such as data quality issues and poor code portability, and propose possible solutions and development paths. These will provide clear guidance for future research and technological reiteration.