Traditional MIC-based antibiotic susceptibility testing often misses sub-resistant bacteria, including persistent, tolerant, and heteroresistant populations, which may contribute to treatment failure and resistance evolution. Researchers at Huazhong University of Science and Technology have developed DP-AST, a deep learning-based microfluidic rapid phenotypic AST platform inspired by the Dujiangyan irrigation system. DP-AST measures MIC, bacterial growth activity, and concentration-effect curves to identify hidden sub-resistant bacteria. The study also identified gadE as a key regulator of amikacin sub-resistance in E. coli, offering new insights for resistance mechanism studies and future clinical diagnostics.Traditional MIC-based antibiotic susceptibility testing often misses sub-resistant bacteria, including persistent, tolerant, and heteroresistant populations, which may contribute to treatment failure and resistance evolution. Researchers at Huazhong University of Science and Technology have developed DP-AST, a deep learning-based microfluidic rapid phenotypic AST platform inspired by the Dujiangyan irrigation system. DP-AST measures MIC, bacterial growth activity, and concentration-effect curves to identify hidden sub-resistant bacteria. The study also identified gadE as a key regulator of amikacin sub-resistance in E. coli, offering new insights for resistance mechanism studies and future clinical diagnostics.Traditional MIC-based antibiotic susceptibility testing often misses sub-resistant bacteria, including persistent, tolerant, and heteroresistant populations, which may contribute to treatment failure and resistance evolution. Researchers at Huazhong University of Science and Technology have developed DP-AST, a deep learning-based microfluidic rapid phenotypic AST platform inspired by the Dujiangyan irrigation system. DP-AST measures MIC, bacterial growth activity, and concentration-effect curves to identify hidden sub-resistant bacteria. The study also identified gadE as a key regulator of amikacin sub-resistance in E. coli, offering new insights for resistance mechanism studies and future clinical diagnostics.

A new computation-guided framework for exploring aggregation-induced emission (AIE) luminogens has been developed by researchers from Japan. Quantum chemical calculations, organic synthesis, and advanced spectroscopy revealed how the introduction of donor and acceptor groups and their substitution positions shape excited-state dynamics in bridged stilbene derivatives. AIE behavior can be predicted from a small number of computational descriptors, offering a rational strategy for designing luminescent AIE materials for applications such as OLEDs, bioimaging, and sensors.

The truck developed by the Department of Mechanical Engineering of Politecnico di Milano is a mobile factory that brings on-site treatment of composite materials to major events, companies and remote areas

Researchers at Duke University built one of the first large-scale datasets of stablecoin transfers and used it to train machine-learning models that flag suspicious cryptocurrency wallets. Published in the Blockchain Journal, the study detects illicit activity with high accuracy and, importantly, tells apart different types of criminal behavior, helping compliance teams act on real threats while sparing innocent users from frozen assets.