Detection of Viable but Nonculturable E. coli Induced by Low-Level Antimicrobials Using AI-Enabled Hyperspectral Microscopy.

Rapid detection of bacterial pathogens is essential for food safety and public health, yet bacteria can evade detection by entering a viable but nonculturable (VBNC) state under sublethal stress, such as antimicrobial residues. These bacteria remain active but undetectable by standard culture-based methods without extensive enrichment, necessitating advanced detection methods. This study developed an AI-enabled hyperspectral microscope imaging (HMI) framework for rapid VBNC detection under low-level antimicrobials.

Impact of Biotic and Abiotic Factors on , , and Enterohemorrhagic in Agricultural Soil Extracts.

Outbreaks of Enterohemorrhagic (EHEC), , and linked to fresh produce consumption pose significant food safety concerns. These pathogens can contaminate pre-harvest produce through various routes, including contaminated water. Soil physicochemical properties and flooding can influence pathogen survival in soils.

Digital Mechanical Metamaterial: Encoding Mechanical Information with Graphical Stiffness Pattern for Adaptive Soft Machines.

Inspired by the adaptive features exhibited by biological organisms like the octopus, soft machines that can tune their shape and mechanical properties have shown great potential in applications involving unstructured and continuously changing environments. However, current soft machines are far from achieving the same level of adaptability as their biological counterparts, hampered by limited real-time tunability and severely deficient reprogrammable space of properties and functionalities. As a steppingstone toward fully adaptive soft robots and smart interactive machines, an encodable multifunctional material that uses graphical stiffness patterns is introduced here to in situ program versatile mechanical capabilities without requiring additional infrastructure.

AI-enabled biosensing for rapid pathogen detection: From liquid food to agricultural water.

Rapid pathogen detection in food and agricultural water is essential for ensuring food safety and public health. However, complex and noisy environmental background matrices delay the identification of pathogens and require highly trained personnel. Here, we present an AI-biosensing framework for accelerated and automated pathogen detection in various water samples, from liquid food to agricultural water.

Accelerating the Detection of Bacteria in Food Using Artificial Intelligence and Optical Imaging.

In assessing food microbial safety, the presence of Escherichia coli is a critical indicator of fecal contamination. However, conventional detection methods require the isolation of bacterial macrocolonies for biochemical or genetic characterization, which takes a few days and is labor-intensive. In this study, we show that the real-time object detection and classification algorithm You Only Look Once version 4 (YOLOv4) can accurately identify the presence of E.

Modeling bioaffinity-based targeted delivery of antimicrobials to Escherichia coli biofilms using yeast microparticles. Part I: Model development and numerical simulation.

Biofilms are potential reservoirs for pathogenic microbes leading to a significant challenge for food safety, ecosystems, and human health. Various micro-and nanoparticles have been experimentally evaluated to improve biofilm inactivation by targeted delivery of antimicrobials. However, the role of transport processes and reaction kinetics of these delivery systems are not well understood.

Modeling bioaffinity-based targeted delivery of antimicrobials to Escherichia coli biofilms using yeast microparticles. Part II: Parameter evaluation and validation.

The design of bioaffinity-based targeted delivery systems for biofilm inactivation may require a comprehensive understanding of physicochemical and biochemical properties of biobased antimicrobial particles and their interactions with biofilm. In this study, Escherichia coli biofilm inactivation by chlorine-charged yeast microparticles was numerically simulated, and the roles of chemical stability, binding affinity, and controlled release of this targeted delivery system were assessed using this numerical simulation. The simulation results were experimentally validated using two different types of yeast microparticles.

Chlorine Rechargeable Biocidal -Halamine Nanofibrous Membranes Incorporated with Bifunctional Zwitterionic Polymers for Efficient Water Disinfection Applications.

An intrinsically hydrophilic nanofibrous membrane with chlorine rechargeable biocidal and antifouling functions was prepared by using a combination of chemically bonded -halamine moieties and zwitterionic polymers (PEI-S). The designed nanofibrous membrane, named as PEI-S@BNF-2 h, can exhibit integrated features of reduced bacterial adhesion, rechargeable biocidal activity, and easy release of killed bacteria by using mild hydrodynamic forces. The representative functional performances of the PEI-S@BNF-2 h membrane include high active chlorine capacity (>4000 ppm), large specific surface area, ease of chlorine rechargeability, long-term stability, and exceptional biocidal activity (99.