Single-atom nanozymes (SAzymes) hold significant potential for tumor catalytic therapy, but their effectiveness is often compromised by low catalytic efficiency within tumor microenvironment. This efficiency is mainly influenced by key factors including hydrogen peroxide (H₂O₂) availability, acidity, and temperature. Simultaneous optimization of these key factors presents a significant challenge for tumor catalytic therapy. In this study, we developed a comprehensive strategy to refine single-atom catalytic kinetics for enhancing tumor catalytic therapy through dual-enzyme-driven cascade reactions. Iridium (Ir) SAzymes with high catalytic activity and natural enzyme glucose oxidase (GOx) were utilized to construct the cascade reaction system. GOx was loaded by Ir SAzymes due to its large surface area. Then, the dual-enzyme-driven cascade reaction system was modified by cancer cell membranes for improving biocompatibility and achieving tumor homologous targeting ability. GOx catalysis reaction could produce abundant H₂O₂ and lower the local pH, thereby optimizing key reaction-limiting factors. Additionally, upon laser irradiation, Ir SAzymes could raise local temperature, further enhancing the catalytic efficiency of dual-enzyme system. This comprehensive optimization maximized the performance of Ir SAzymes, significantly improving the efficiency of catalytic therapy. Our findings present a strategy of refining single-atom catalytic kinetics for tumor homologous-targeted catalytic therapy.

Four years after pre-surgery treatment with a novel combination of immune checkpoint inhibitors, nivolumab and relatlimab, 87% of patients with stage III melanoma remained alive, according to new results from a study led by researchers at The University of Texas MD Anderson Cancer Center.

A breast scan for detecting cancer takes less than a minute using an experimental system that combines photoacoustic and ultrasound imaging, according to a study in IEEE Transactions on Medical Imaging. The system does not require painful compression like mammography. In tests, it produced clear, artificial intelligence-powered 3D images of common breast cancer subtypes such as Luminal A, Luminal B and Triple-Negative Breast Cancer.