The growing threat of antimicrobial resistance (AMR) calls for effective solutions such as phage therapy. In China, phage therapy has been implemented in over 30 hospitals, treating more than 500 patients with multidrug-resistant infections. Phage therapy can currently be developed through two pathways: as a novel biomedical technology or as a new drug. However, regulatory ambiguity persists due to a lack of standardized quality criteria and clear approval frameworks. This article proposes a "standards first, pathway pilots, and industry cultivation" strategy. Key recommendations include developing national quality control guidelines for therapeutic phage preparations and clarifying the detailed guidelines for approval policies for new biomedical technologies, biologics, and advanced therapy medicinal products (ATMPs). These efforts aim to enable safe and effective clinical translation, positioning China as a frontrunner in phage therapy and making significant contributions to the global response against AMR.

In a paper published in the international peer-reviewed journal Mycology, a research team led by Professor Yi Wang and Professor Weiming Zhu from Ocean University of China reports a novel epigenetic strategy to efficiently upregulate the biosynthesis of Monascus pigments (MPs), a widely used natural colorant. By disrupting the Ash2 gene in the acidophilic fungus Talaromyces purpurogenus OUCMDZ-019, the team achieved robust activation of MPs biosynthetic pathways, discovered four new azaphilone pigments, and confirmed the strain is free of the harmful mycotoxin citrinin, providing a safe and high-performance solution for the industrial production of natural pigments.

The stress concentration and damage evolution of ultra-high performance concrete (UHPC) under long-term dynamic loading are difficult to monitor in real time, as conventional sensors suffer from poor durability, high cost, and incompatibility with matrix deformation. Recently, a team from the University of Shanghai for Science and Technology developed a machine learning framework that significantly improves dynamic compressive stress prediction in high-sensitivity ultra-high performance concrete (HS-UHPC) by incorporating electrical resistivity as a key input parameter. Using three machine learning algorithms—double-layer neural network, boosting tree, and squared exponential Gaussian process regression (SE-GPR)—the team demonstrated that adding resistivity measurements alongside traditional displacement data enhances predictive accuracy, with the SE-GPR model achieving an R² of 0.85 and reducing mean absolute error by 41.1% compared to displacement-only models. The core innovation is using electrical resistivity to directly capture load‑induced microstructural changes, overcoming the damage‑detection limitations of traditional strain or displacement measurements. This provides a new theoretical basis for intelligent monitoring of self‑sensing concrete.

A research team investigated how high-volume fly ash replacement affects concrete’s early-age characteristics and hardening properties. Tests with 0%–60% fly ash replacing cement show that fly ash improves flowability but delays early hydration. While early strength drops with higher fly ash, 10%–40% replacement delivers superior long-term mechanical performance. The team recommends 40% fly ash as the optimal cement replacement, balancing environmental sustainability, cost efficiency, and structural performance for green concrete engineering.