Salmonella is a widely distributed foodborne pathogen that poses a serious threat to public health. Traditional detection methods suffer from limitations such as being time-consuming, complex, and reliant on expensive equipment, making them unsuitable for rapid and on-site detection. In recent years, nanomaterial-based optical biosensors have emerged as a research hotspot in Salmonella detection due to their high sensitivity, strong specificity, fast response, and portability. These sensors offer new technological approaches for real-time monitoring and early warning of pathogens. This paper reviews the research progress of nanomaterial-based optical biosensors for Salmonella detection over the past five years, systematically summarizing the design principles, performance characteristics, and applications of different biorecognition elements and optical signal components in Salmonella detection. It also highlights the selection and integrated design strategies of combining different biorecognition elements with optical signal components to improve detection sensitivity, shorten detection time, and enhance specificity, providing a theoretical reference for researchers in this field. Finally, the paper analyzes the strengths and weaknesses of current technologies and discusses future development directions of nanomaterial-based optical biosensors for Salmonella detection, aiming to advance the technology and provide more efficient and reliable solutions for public health security.

Correction for 'Single glucose molecule transport process revealed by force tracing and molecular dynamics simulations' by Yangang Pan et al., Nanoscale Horiz., 2018, 3, 517-524, https://doi.org/10.1039/C8NH00056E.

Identifying live foodborne bacteria is essential for ensuring food safety and preventing foodborne illnesses. This study investigated the use of hyperspectral microscope imaging and deep learning methods to accurately distinguish between live and dead foodborne bacteria based on their spectral and morphological features. Three deep learning models, Fusion-Net I, II, and III, were developed and evaluated for their ability to classify live and dead bacterial cells of six pathogenic strains, including Escherichia coli (EC), Listeria innocua (LI), Staphylococcus aureus (SA), Salmonella Enteritidis (SE), Salmonella Heidelberg (SH), and Salmonella Typhimurium (ST). The models utilized both morphological and spectral characteristics of the bacterial cells, with inputs of average spectra and 546 nm band images. Fusion-Net I achieved high accuracy in identifying live bacterial cells, with a classification accuracy of 100% for LI, SE, ST strains and over 92.9% for EC, SA, SH. Fusion-Net II and III models were even more robust, achieving 100% accuracy consistently in classifying dead cells in all six strains. Fusion-Net III also showed the ability to identify bacterial strains with 96.9% accuracy, making it a dual-task model with potential applications in identifying live foodborne bacteria prior to foodborne outbreaks. These findings suggest that the use of hyperspectral microscope imaging and deep learning methods could provide a new tool for quickly and accurately identifying bacterial viability, thereby improving the efficiency and reliability of food safety inspection.

Salmonella is commonly found on broiler chickens during processing. This study investigates the Salmonella detection method that reduces the necessary time for confirmation, by collecting surface-enhanced Raman spectroscopy (SERS) spectra from bacteria colonies, applied to a substrate of biopolymer encapsulated AgNO3 nanoparticles. Chicken rinses containing Salmonella Typhimurium (ST) were analyzed by SERS and compared to traditional plating and PCR analyses. SERS spectra from confirmed ST and non-Salmonella colonies appear similar in spectra composition, but with different peak intensities. t-Test on the peak intensities showed that ST and non-Salmonella colonies were significantly different (α = 0.0045) at 5 peaks, 692 cm-1, 718 cm-1, 791 cm-1, 859 cm-1, and 1018 cm-1. A support vector machine (SVM) classification algorithm was able to separate ST and non-Salmonella samples with an overall classification accuracy of 96.7 %.

Developments of portable biosensors for field-deployable detections have been increasingly important to control foodborne pathogens in regulatory environment and in early stage of outbreaks. Conventional cultivation and gene amplification methods require sophisticated instruments and highly skilled professionals; while portable biosensing devices provide more freedom for rapid detections not only in research laboratories but also in the field; however, their sensitivity and specificity are limited. Microfluidic methods have the advantage of miniaturizing instrumental size while integrating multiple functions and high-throughput capability into one streamlined system at low cost. Minimal sample consumption is another advantage to detect samples in different sizes and concentrations, which is important for the close monitoring of pathogens at consumer end. They improve measurement or manipulation of bacteria by increasing the ratio of functional interface of the device to the targeted biospecies and in turn reducing background interference. This article introduces the major active and passive microfluidic devices that have been used for bacteria sampling and biosensing. The emphasis is on particle-based sorting/enrichment methods with or without external physical fields applied to the microfluidic devices and on various biosensing applications reported for bacteria sampling. Three major fabrication methods for microfluidics are briefly discussed with their advantages and limitations. The applications of these active and passive microfluidic sampling methods in the past 5 years have been summarized, with the focus on Escherichia coli and Salmonella. The current challenges to microfluidic bacteria sampling are caused by the small size and nonspherical shape of various bacterial cells, which can induce unpredictable deviations in sampling and biosensing processes. Future studies are needed to develop rapid prototyping methods for device manufacturing, which can facilitate rapid response to various foodborne pathogen outbreaks.

Atomic force microscopy-single-molecule force spectroscopy (AFM-SMFS) is a powerful methodology to probe intermolecular and intramolecular interactions in biological systems because of its operability in physiological conditions, facile and rapid sample preparation, versatile molecular manipulation, and combined functionality with high-resolution imaging. Since a huge number of AFM-SMFS force-distance curves are collected to avoid human bias and errors and to save time, numerous algorithms have been developed to analyze the AFM-SMFS curves. Nevertheless, there is still a need to develop new algorithms for the analysis of AFM-SMFS data since the current algorithms cannot specify an unbinding force to a corresponding/each binding site due to the lack of networking functionality to model the relationship between the unbinding forces. To address this challenge, herein, we develop an unsupervised method, i.e., a network-based automatic clustering algorithm (NASA), to decode the details of specific molecules, e.g., the unbinding force of each binding site, given the input of AFM-SMFS curves. Using the interaction of heparan sulfate (HS)-antithrombin (AT) on different endothelial cell surfaces as a model system, we demonstrate that NASA is able to automatically detect the peak and calculate the unbinding force. More importantly, NASA successfully identifies three unbinding force clusters, which could belong to three different binding sites, for both Ext1(f/f) and Ndst1(f/f) cell lines. NASA has great potential to be applied either readily or slightly modified to other AFM-based SMFS measurements that result in "saw-tooth"-shaped force-distance curves showing jumps related to the force unbinding, such as antibody-antigen interaction and DNA-protein interaction.

The frequent outbreaks of foodborne pathogens have stimulated the demand of biosensors capable of rapid and multiplex detection of contaminated food. In this study, surface plasmon resonance imaging (SPRi) was used in simultaneous label-free detection of multiple foodborne pathogens, mainly Salmonella spp. and Shiga-toxin producing Escherichia coli (STEC), in commercial chicken carcass rinse. The antibodies were immobilized on the same SPRi sensor chip as a label-free immunoassay. Their immobilization concentrations were optimized to be ranging from 0.25 to 1.0 mg/mL, and independent of pH values. This label-free immunoassay achieved 10(6) colony-forming unit (CFU)/mL limit of detection for Salmonella, which was further improved to 1.0 CFU/mL with overnight bacteria enrichment. The injected samples with different bacteria, Salmonella Enteritidis, STEC, and Listeria monocytogenes, have been identified by the same biochip. Moreover, the SPRi signals revealed complex interference effects among coexisting bacteria species in heterogeneous bacteria solutions. This SPRi-based immunoassay demonstrates the great potential in high-throughput screening of multiple pathogenic bacteria coexisting in chicken carcass rinse. The reliability of antibody immobilization and cross-reactions of different antibodies on the same biochip are the major challenges of practical application of SPRi.

Shiga toxin-producing Escherichia coli (STEC) are responsible for gastrointestinal diseases reported in numerous outbreaks around the world as well as in the United States. Current detection methods have limitation to implement for rapid field-deployable detection with high volume of samples that are needed for regulatory purposes. Surface plasmon resonance imaging (SPRi) has proved to achieve rapid and label-free screening of multiple pathogens simultaneously, so it was evaluated in this work for the detection of Shiga toxins (Stx1a and Stx2a toxoids were used as the less toxic alternatives to Stx1 and Stx2, respectively). Multiple antibodies (Stx1pAb, Stx1-1mAb, Stx1-2mAb, Stx1d-3mAb, Stx1e-4mAb, Stx2pAb, Stx2-1mAb, Stx2-2mAb, and Stx2-10mAb) were spotted one by one by programed microarrayer, on the same high-throughput biochip with 50-nm gold film through multiple crosslinking and blocking steps to improve the orientation of antibodies on the biochip surface. Shiga toxins were detected based on the SPRi signal difference (ΔR) between immobilized testing antibodies and immunoglobulin G (IgG) control. Among the antibodies tested, Stx1pAb showed the highest sensitivity for Stx1 toxoid, with the limit of detection (LOD) of 50 ng/mL and detection time of 20 min. Both Stx2-1mAb and Stx2-2mAb exhibited high sensitivity for Stx2 toxoid. Furthermore, gold nanoparticles (GNPs) were used to amplify the SPRi signals of monoclonal antibodies in a sandwich platform. The LOD reached the level of picogram (pg)/mL with the help of GNP-antibody conjugate. This result proved that SPRi biochip with selected antibodies has the potential for rapid, high-throughput and multiplex detection of Shiga toxins.

Surface plasmon resonance imaging (SPRi) has been increasingly used in the label-free detections of various biospecies, such as organic toxins, proteins, and bacteria. In combination with the well-developed microarray immunoassay, SPRi has the advantages of rapid detection in tens of minutes and multiplex detection of different targets with the same biochip. Both prism-based and prism-free configurations of SPRi have been developed for highly integrated portable immunosensors, which have shown great potential on pathogen detection and living cell imaging. This review summarizes the recent advances in immunoassay biosensing with SPRi, with special emphasis on the multiplex detections of foodborne pathogens. Additionally, various spotting techniques, surface modification protocols, and signal amplification methods have been developed to improve the specificity and sensitivity of the SPRi biochip. The challenges in multiplex detections of foodborne pathogens in real-world samples are addressed, and future perspectives of miniaturizing SPRi immunosensors with nanotechnologies are discussed.

To study the genetic structure of the Stenotrophomonas CRISPR-Cas system using bioinformatics methods. Methods The sequence information of all Stenotrophomonas strains published in the CRISPRdb database was collected, and the CRISPR locus was analyzed using the CRISPRFinder software; All spacers were searched by BLAST platform in the PubMed database to find homologous sequences, and then the relationship between the number of spacer sequences and the number of phages was statistically analyzed. Results According to statistics, 15 confirmed CRISPR structures and 132 questionable CRISPRs were found in 26 strains of Stenotrophomonas, and the repeat sequences of CRISPR structures in different strains were more conservative. Only 1.3% of spacers were homologous with the sequences of known bacteriophages or plasmid in NCBI database. Conclusion The targeted genes of the spacer sequences are mainly from the genome of the bacteria, indicating that the evolution of the Stenotrophomonas CRISPR is related to other bacterial genes. In addition, the negative correlation between the spacer sequence and the number of phages indicates that CRISPR can prevent phage invasion. Analysis of the structure of CRISPR loci in the genome of Stenotrophomonas laid the foundation for further study of drug resistance and genomic stability.

PrP-lipid membrane interactions are critical to PrP structural conversion and neurotoxicity, but its molecular mechanism remains unclear. A two-dimensional histogram of force-distance curves and a worm-like chain model revealed three binding regions at the PrP N-terminal, providing the molecular basis for understanding the interactions between full-length PrP and lipid membranes.

Transporting individual molecules across cell membranes is a fundamental process in cellular metabolism. Although the crystal diffraction technique has greatly contributed to our understanding of the structures of the involved transporters, a description of the dynamic transport mechanism at the single-molecule level has been extremely elusive. In this study, we applied atomic force microscopy (AFM)-based force tracing to directly monitor the transport of a single molecule, d-glucose, across living cell membranes. Our results show that the force to transport a single molecule of d-glucose across cell membranes is 37 ± 9 pN, and the corresponding transport interval is approximately 20 ms, while the average speed is approximately 0.3 μm s-1. Furthermore, our calculated force profile from molecular dynamics simulations showed quantitatively good agreement with the force tracing observation and revealed detailed information regarding the glucose transport path, indicating that two salt bridges, K38/E299 and K300/E426, play critical roles during glucose transport across glucose transporter 1 (GLUT1). This role was further verified using biological experiments that disrupted these two bridges and measured the uptake of glucose into the cells. Our approaches led to the first unambiguous description of the glucose transport process across cell membranes at the single-molecule level and demonstrated the biological importance of the two salt bridges for transporting glucose across GLUT1.

The emerging nanotechnologies have greatly facilitated the development of label-free biosensors. The atomic force microscopy (AFM) has been used to study the molecular mechanism of the reactions for protein and aptamers. The surface plasmon resonance (SPR) have been used in fast detections of various pathogens such as bacteria. This study used both AFM and SPR to investigate the complex reactions between aptamers and outer membrane proteins (OMPs) on the surface of S. typhimurium.

The electrostatic surface potential (ESP) of prion oligomers has critical influences on the aggregating processes of the prion molecules. The atomic force microscopy (AFM) and structural simulation were combined to investigate the molecular basis of the full-length human recombinant prion oligomerization on mica surfaces. The high resolution non-intrusive AFM images showed that the prion oligomers formed different patterns on mica surfaces at different buffer pH values. The basic binding units for the large oligomers were determined to be prion momoners (Ms), dimers (Ds), and trimers (Ts). The forming of the D and T units happened through the binding of hydrophobic β-sheets of the M units. In contrast, the α-helices of these M, D, and T units were the binding areas for the formation of large oligomers. At pH 4.5, the binding units M, D, and T showed clear polarized ESP distributions on the surface domains, while at pH 7.0, they showed more evenly distributed ESPs. Based on the conformations of oligomers observed from AFM images, the D and T units were more abundantly on mica surface at pH 4.5 because the ESP re-distribution of M units helped to stabilize these larger oligomers. The amino acid side chains involved in the binding interfaces were stabilized by hydrogen bonds and electrostatic interactions. The detailed analysis of the charged side chains at pH 4.5 indicated that the polarized ESPs induced the aggregations among M, D, and T to form larger oligomers. Therefore, the hydrogen bonds and electrostatic interactions worked together to form the stabilized prion oligomers.

The aggregates of the full-length human recombinant prion protein (PrP) (23-231) on model membranes were investigated by combining the atomic force microscopy (AFM) measurements and theoretical calculations at pH 5.0, showing the great effect of PrP concentration on its supramolecular assemblies on the lipid bilayer.

The predictability, diversity and programmability of DNA make it a leading candidate for the design of functional electronic devices that use single molecules, yet its electron transport properties have not been fully elucidated. This is primarily because of a poor understanding of how the structure of DNA determines its electron transport. Here, we demonstrate a DNA-based molecular rectifier constructed by site-specific intercalation of small molecules (coralyne) into a custom-designed 11-base-pair DNA duplex. Measured current–voltage curves of the DNA–coralyne molecular junction show unexpectedly large rectification with a rectification ratio of about 15 at 1.1 V, a counter-intuitive finding considering the seemingly symmetrical molecular structure of the junction. A non-equilibrium Green's function-based model—parameterized by density functional theory calculations—revealed that the coralyne-induced spatial asymmetry in the electron state distribution caused the observed rectification. This inherent asymmetry leads to changes in the coupling of the molecular HOMO−1 level to the electrodes when an external voltage is applied, resulting in an asymmetric change in transmission.

By in situ time-lapse AFM, we investigated early-stage aggregates of PrP formed at low concentration (100 ng/mL) on mica and Au(111) surfaces in acetate buffer (pH 4.5). Remarkably different PrP assemblies were observed. Oligomeric structures of PrP aggregates were observed on mica surface, which was in sharp contrast to the multi-layer PrP aggregates yielding parallel linear patterns observed Au(111) surface. Combining molecular dynamics and docking simulations, PrP monomers, dimers and trimers were revealed as the basic units of the observed aggregates. Besides, the mechanisms of the observed PrP aggregations and the corresponding molecular-substrate and intermolecular interactions were suggested. These interactions involved gold-sulfur interaction, electrostatic interaction, hydrophobic interaction, and hydrogen binding interaction. In contrast, the PrP aggregates observed in pH 7.2 PBS buffer demonstrated similar large ball-like structures on both mica and Au(111) surfaces. The results indicate that the pH of a solution and the surface of the system can have strong effects on supramolecular assemblies of prion proteins. This study provides in-depth understanding on the structural and mechanistic nature of PrP aggregation, and can be used to study the aggregation mechanisms of other proteins with similar misfolding properties.