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.

Streptococcus suis serotype 2 (SS2) is an emerging human pathogen worldwide. A large outbreak occurred in the summer of 2005 in China. Serum samples from this outbreak revealed that levels of the main proinflammatory cytokines were significantly higher in patients with streptococcal toxic-shock-like syndrome (STSLS) than in patients with meningitis only. However, the mechanism underlying the cytokine storm in STSLS caused by SS2 remained unclear. In this study, we found that suilysin (SLY) is the main protein inflammatory stimulus of SS2 and that native SLY (nSLY) stimulated cytokines independently of its haemolytic ability. Interestingly, a small amount of SLY (Å Mol/L) induced strong, long-term TNF-α release from human PBMCs. We also found that nSLY stimulated TNF-α in wild-type macrophages but not in macrophages from mice that carried a spontaneous mutation in TLR4 (P712H). We demonstrated for the first time that SLY stimulates immune cells through TLR4. In addition, the Myd88 adaptor-p38-MAPK pathway was involved in this process. The present study suggested that the TLR4-dependent inflammatory responses induced by SLY in host might contribute to the STSLS caused by SS2 and that p38-MAPK could be used as a target to control the release of excess TNF-α induced by SS2.

Coronavirus disease 2019 (COVID-19) threatens global public health and economy. In order to develop safe and effective vaccines, suitable animal models must be established. Here we report the rapid adaption of SARS-CoV-2 in BALB/c mice, based on which a convenient, economical and effective animal model was developed. Specifically, we found that mouse-adapted SARS-CoV-2 at passage 6 (MACSp6) efficiently infected both aged and young wild-type BALB/c mice, resulting in moderate pneumonia as well as inflammatory responses. The elevated infectivity of MACSp6 in mice could be attributed to the substitution of a key residue (N501Y) in the receptorbinding domain (RBD). Using this novel animal model, we further evaluated the in vivo protective efficacy of an RBD-based SARS-CoV-2 subunit vaccine, which elicited highly potent neutralizing antibodies and conferred full protection against SARS-CoV-2 MACSp6 challenge. This novel mouse model is convenient and effective in evaluating the in vivo protective efficacy of SARS-CoV-2 vaccine. Summary: This study describes a unique mouse model for SARS-CoV-2 infection and confirms protective efficacy of a SARS-CoV-2 RBD subunit vaccine.

Electrochemical Immunosensing (EI) combines electrochemical analysis and immunology principles and is characterized by its simplicity, rapid detection, high sensitivity, and specificity. EI has become an important approach in various fields, such as clinical diagnosis, disease prevention and treatment, environmental monitoring, and food safety. However, EI multi-component detection still faces two major bottlenecks: first, the lack of cost-effective and portable detection platforms; second, the difficulty in eliminating batch differences and accurately decoupling signals from multiple analytes. With the gradual maturation of biochip technology, high-throughput analysis and portable detection utilizing the advantages of miniaturized chips, high sensitivity, and low cost have become possible. Meanwhile, Artificial Intelligence (AI) enables accurate decoupling of signals and enhances the sensitivity and specificity of multi-component detection. We believe that by evaluating and analyzing the characteristics, benefits, and linkages of EI, biochip, and AI technologies, we may considerably accelerate the development of EI multi-component detection. Therefore, we propose three specific prospects: first, AI can enhance and optimize the performance of the EI biochips, addressing the issue of multi-component detection for portable platforms. Second, the AI-enhanced EI biochips can be widely applied in home care, medical healthcare, and other areas. Third, the cross-fusion and innovation of EI, biochip, and AI technologies will effectively solve key bottlenecks in biochip detection, promoting interdisciplinary development. However, challenges may arise from AI algorithms that are difficult to explain and limited data access. Nevertheless, we believe that with technological advances and further research, there will be more methods and technologies to overcome these challenges.

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.