We analyzed fungal communities in kidney tumors from 1,044 patients across four international studies. Patients with abundant tumor fungi had worse survival outcomes and reduced response to immunotherapy. These fungi may suppress fat breakdown processes and weaken immune T cells that fight cancer. We developed predictive tools using fungal signatures that accurately forecast treatment success in kidney and other cancers. One fungal species, Aspergillus tanneri, was particularly linked to poor outcomes.

The limitations of conventional electromagnetic wave (EMW) absorbing materials in terms of high-temperature resistance have stimulated interest in the development of high-temperature EMW absorbing materials across various fields. However, due to the temperature dependence of the permittivity, achieving effective EMW absorption across a wide temperature range remains a significant challenge for high-temperature EMW absorbing materials. Herein, a novel molecular-scale strategy is proposed for in-situ construction multi-heterointerface during the polymer-derived ceramics process, thereby achieving temperature-insensitive permittivity. This approach to developing temperature-insensitive dielectric ceramics significantly improves the performance and functionality of high-temperature EMW absorbing materials, thereby providing substantial guidance and reference value.

Dissolved organic matter (DOM) plays a critical role in nutrient cycling and microbial dynamics across ecosystems, but its complexity poses major analytical challenges. To address this, a team led by Prof. Jianjun Wang from the Chinese Academy of Sciences has developed iDOM, a powerful new R package for the statistical analysis and visualization of DOM data. Designed to integrate DOM composition with environmental and microbial factors, iDOM enables researchers to unravel the ecological processes driving DOM dynamics under global change. With tools for trait analysis, diversity metrics, network inference, and temperature response modeling, iDOM offers a robust and reproducible framework for DOM studies worldwide.

Artificial intelligence (AI) is transforming healthcare across multiple fields, and prostate cancer (PCa)  is no exception. A recent review conducted by researchers discusses the role of AI in clinical practice against PCa. According to this study, AI models enable early detection of PCa with high accuracy while minimizing errors. AI models used in molecular subtyping and precision medicine also offer personalized treatments—improving the overall quality of life of patients.