Huiqi Duan

Artificial intelligence (AI) is transforming electrochemical biosensing systems, offering novel solutions for foodborne pathogen detection. This review examines the integration of AI technologies, particularly machine learning and deep learning algorithms, in enhancing sensor design, material optimization, and signal processing for detecting key pathogens such as Escherichia coli, Salmonella, and Staphylococcus aureus. Key advancements include improved sensitivity, multiplexed detection, and adaptability to complex environments. The application of AI to the design of recognition molecules (e.g., enzymes, antibodies, aptamers), as well as to electrochemical parameter tuning and multicomponent signal analysis, is systematically reviewed. Additionally, the convergence of AI with the Internet of Things (IoT) is discussed as a pathway to portable, real-time detection platforms. The review highlights the pivotal role of AI across multiple layers of biosensor development, emphasizing the opportunities and challenges that arise from interdisciplinary integration and the practical deployment of IoT-enabled technologies in electrochemical sensing systems. Despite significant progress, challenges remain in data quality, model generalization, and interpretability. The review concludes by outlining future research directions for building robust, intelligent biosensing systems capable of supporting scalable food safety monitoring.

Nipah virus (NiV) is an emerging zoonotic pathogen whose surface glycoprotein (G)-mediated host cell invasion mechanism leads to fatal encephalitis in infected patients (case fatality rate 40–75%). Given the limitations of existing diagnostic technologies, such as low sensitivity and prolonged processing times, we prepared an anti-NiV-G monoclonal antibody to establish a novel Amplified Luminescent Proximity Homogeneous Assay (AlphaLISA) detection system. Firstly, five high-affinity anti-NiV-G monoclonal antibodies were screened from the spleens of immunized mice by flow cytometry-single-cell cloning technology. The reaction system was further optimized, and the optimal dilution ratio of antibody-conjugated receptor microspheres, biotinylated antibodies, and donor microspheres was screened, and the AlphaLISA detection platform was successfully constructed. The detection sensitivity of NiV-G protein was 0.024 ng/mL (41.7 times higher than that of conventional ELISA), the coefficient of variation was <9.5%, and the repetition was good. It showed good specificity in the detection of 5 zoonotic viruses, including Japanese encephalitis virus and Zika virus. At the same time, this method is less disturbed by human serum, and the detection time is less than 30 min, showing a good clinical application prospect.

This paper reviews the field of cascaded metasurfaces, which are advanced optical devices formed by stacking or serially arranging multiple metasurface layers. These structures leverage near-field and far-field electromagnetic (EM) coupling mechanisms to enhance functionalities beyond single-layer metasurfaces. This review comprehensively discusses the physical principles, design methodologies, and applications of cascaded metasurfaces, focusing on both static and dynamic configurations. Near-field-coupled structures create new resonant modes through strong EM interactions, allowing for efficient control of light properties like phase, polarization, and wave propagation. Far-field coupling, achieved through greater interlayer spacing, enables traditional optical methods for design, expanding applications to aberration correction, spectrometers, and retroreflectors. Dynamic configurations include tunable devices that adjust their optical characteristics through mechanical motion, making them valuable for applications in beam steering, varifocal lenses, and holography. This paper concludes with insights into the potential of cascaded metasurfaces to create multifunctional, compact optical systems, setting the stage for future innovations in miniaturized and integrated optical devices.

Group B Streptococcus (GBS) is a life-threatening opportunistic pathogen, particularly in pregnant women, infants, and the elderly. Currently, maternal vaccination is considered the most viable long-term option for preventing GBS mother-to-infant infection, and two polysaccharide conjugate vaccines utilizing CRM197 as a carrier protein have undergone clinical phase II trials. Surface immunogenic protein (Sip), present in all identified serotypes of GBS strains so far, is a protective surface protein of GBS. In this study, the type Ia capsular polysaccharide (CPS) of GBS was utilized as a model to develop candidate antigens for a polysaccharide conjugate vaccine by coupling it with the Sip of GBS and the traditional carrier protein CRM197. Serum analysis from immunized New Zealand rabbits and CD1 mice revealed that there was no significant difference in antibody titers between the Ia-Sip group and Ia-CRM197 group; however, both were significantly higher than those observed in the Ia polysaccharide group. Opsonophagocytosis and passive immune protection results using rabbit serum indicated no significant difference between the Ia-Sip and Ia-CRM197 groups, both outperforming the Ia polysaccharide group. Furthermore, serum from the Ia-Sip group had a cross-protective effect on multiple types of GBS strains. The challenge test results in CD1 mice demonstrated that the Ia-Sip group provided complete protection against lethal doses of bacteria and also showed cross-protection against type III strain. Our study demonstrates for the first time that Ia-Sip is immunogenic and provides serotype-independent protection in glycan conjugate vaccines, which also indicates Sip may serve as an excellent carrier protein for GBS glycan conjugate vaccines and provide cross-protection against multiple GBS strains.