Deep learning assisted PfAgo-programmable genetic circuit for ultrasensitive visual detection of foodborne pathogen in one-tube.

The incidence of foodborne diseases has increased over the years and resulted in major public health problem globally. Foodborne pathogens can be found in various foods and it is important to detect foodborne pathogens to provide safe food supply and to prevent foodborne diseases. The conventional methods used to detect foodborne pathogen are time consuming and laborious. Hence, a variety of methods have been developed for rapid detection of foodborne pathogens as it is required in many food analyses. Rapid detection methods can be categorized into nucleic acid-based, biosensor-based and immunological-based methods. This review emphasizes on the principles and application of recent rapid methods for the detection of foodborne bacterial pathogens. Detection methods included are simple polymerase chain reaction (PCR), multiplex PCR, real-time PCR, nucleic acid sequence-based amplification (NASBA), loop-mediated isothermal amplification (LAMP) and oligonucleotide DNA microarray which classified as nucleic acid-based methods; optical, electrochemical and mass-based biosensors which classified as biosensor-based methods; enzyme-linked immunosorbent assay (ELISA) and lateral flow immunoassay which classified as immunological-based methods. In general, rapid detection methods are generally time-efficient, sensitive, specific and labor-saving. The developments of rapid detection methods are vital in prevention and treatment of foodborne diseases.

Chapter 2 - Modern techniques and developments in the detection of foodborne pathogens

With the increasing emphasis on food safety and health, it has become particularly important to develop rapid, sensitive and low-cost detection methods for foodborne pathogens. Lateral flow assay (LFA) has shown great potential in the field of point-of-care testing (POCT) due to its rapidity, portability and low cost. However, traditional gold nanoparticles (AuNPs)-labeled LFAs face challenges such as insufficient signal strength when detecting nucleic acids. In this study, LFA labeled with polystyrene microspheres was constructed targeting the specific nuc gene of Staphylococcus aureus for the detection of double-stranded PCR products. Unlike traditional AuNPs that pair antibodies through physical adsorption, polystyrene microspheres adopt a covalent coupling strategy, significantly enhancing probe stability and signal strength. Under the optimized conditions, the detection limit was calculated to be 7.28 × 102 CFU/mL, which was approximately 10 times higher than that of the AuNP-based strip. This method demonstrated excellent specificity, reproducibility (RSD < 5%) and stability. In the practical application of artificially contaminated milk samples, the detection performance of polystyrene microsphere-based strips was better than that of AuNP-based strips. This study provides an efficient and easy-to-operate solution for the visual detection of foodborne pathogens.

Microscopic identification of foodborne bacterial pathogens based on deep learning method

It is a public health imperative to have safe food and water across the population. Foodborne infections are one of the primary causes of sickness and mortality in both developed and developing countries. An estimated 100 million foodborne diseases and 120 000 foodborne illness-related fatalities occur each year in India. Several factors affect foodborne illness, such as improper farming methods, poor sanitary and hygienic conditions at all levels of the food supply chain, the lack of preventative measures in the food processing industry, the misuse of food additives, as well as improper storage and handling. In addition, chemical and microbiological combinations also play a key role in disease development. But recent disease outbreaks indicated that microbial pathogens played a major role in the development of foodborne diseases. Therefore, prompt, rapid, and accurate detection of high-risk food pathogens is extremely vital to warrant the safety of the food items. Conventional approaches for identifying foodborne pathogens are labor-intensive and cumbersome. As a result, a range of technologies for the rapid detection of foodborne bacterial pathogens have been developed. Presently, many methods are available for the instantaneous detection, identification, and monitoring of foodborne pathogens, such as nucleic acid-based methods, biosensor-based methods, and immunological-based methods. The goal of this review is to provide a complete evaluation of several existing and emerging strategies for detecting food-borne pathogens. Furthermore, this review outlines innovative methodologies and their uses in food testing, along with their existing limits and future possibilities in the detection of live pathogens in food.

Dual-aptamer-based enzyme linked plasmonic assay for pathogenic bacteria detection

Chapter 1 - Biosensors and Bio‐Based Methods for the Separation and Detection of Foodborne Pathogens

The ability to detect foodborne pathogens and their toxins is important in reducing the role that these pathogens play in human disease. In order to ensure the safety of food and thereby to protect the public health, there is an ever-increasing demand from the food industry, the food consumer, and public-health authorities for more rapid methods to detect foodborne pathogens. Along with pathogen detection, the ability to distinguish between individual pathogens and their products is essential for identifying and understanding the sources of pathogens implicated in diseases. Taken together, discriminatory identification and typing methods provide valuable information that allow scientists to further understand how pathogens pass through the food chain and contribute to infection. To help understand the methods available for the detection and discrimination of foodborne pathogens, this chapter provides coverage of methods for rapid pathogen detection, the detection of foodborne toxins, and an examination of methods that can be used to distinguish among foodborne pathogens.KeywordsPolymerase Chain ReactionFluorescence Resonance Energy TransferPolymerase Chain Reaction AssayToxin GeneMolecular BeaconThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

One-pot dual-CRISPR/Cas12b-mediated dUTP-LAMP for rapid and sensitive detection of foodborne pathogens without carryover contamination

A high gradient and strength bioseparator with nano-sized immunomagnetic particles for specific separation and efficient concentration of E. coli O157:H7

Microfluidic devices for sample preparation and rapid detection of foodborne pathogens

BackgroundAcute kidney injury (AKI) was a common clinical complication among critically ill patients in Intensive Care Unit with high morbidity and mortality. Human liver fatty acid‐binding protein (L‐FABP) as a renal tubular injury biomarker was considered a predictor of AKI; however, high‐throughput and sensitive detection methods were still urgently needed. We constructed a sensitive and rapid detection method for detecting L‐FABP and for exploring the clinical application of L‐FABP as a predictor for AKI.MethodsWe developed an automated detection method of chemiluminescent immunoassay to measure L‐FABP and evaluated the analytical performance of the new methodology including analytical selectivity, analytical sensitivity, linear range, the minimum limit of detection (LOD), repeatability, and accuracy. One hundred patients were enrolled in this study to explore the predictive and diagnostic ability for AKI.ResultsThe chemiluminescent immune‐based L‐FABP assay had outstanding analytical sensitivity including the detection limit of 0.88 ng/ml, and a wide linear range of 2 ng/ml to 160 ng/ml. It also exhibited excellent repeatability with intra‐analysis CVs of 8.73%, 4.72%, and 3.79%, respectively, and the inter‐analysis CVs of 13.47%, 7.28%, and 5.94%, respectively. The recovery rate assay exhibited a good accuracy with three L‐FABP concentration of 99.76%, 102.27%, and 96.92%, respectively. The reference interval of L‐FABP was between 0.88 ng/ml and 5.98 ng/ml. The evaluation of predictive and diagnostic performance showed that higher concentration of L‐FABP indicated higher risk of AKI occurrence and disease progression.ConclusionsThe clinical application of rapid and sensitive detection method of L‐FABP based on the newly developed chemiluminescent immunoassay could offer benefits for patients. L‐FABP was a potentially predictive and diagnostic biomarker for AKI.

A novel biosensor MDC@N-MMCNs to selective detection and elimination of foodborne bacterial pathogens.

Heptaplex-direct PCR assay for simultaneous detection of foodborne pathogens

Proactive management of foodborne illness requires routine surveillance of foodborne pathogens, which requires developing simple, rapid, and sensitive detection methods. Here, a strategy is presented that enables the detection of multiple foodborne bacteria using a 3D nanostructure swab and deep learning-based Raman signal classification. The nanostructure swab efficiently captures foodborne pathogens, and the portable Raman instrument directly collects the Raman signals of captured bacteria. a deep learning algorithm has been demonstrated, 1D convolutional neural network with binary labeling, achieves superior performance in classifying individual bacterial species. This methodology has been extended to mixed bacterial populations, maintaining accuracy close to 100%. In addition, the gradient-weighted class activation mapping method is used to provide an investigation of the Raman bands for foodborne pathogens. For practical application, blind tests are conducted on contaminated kitchen utensils and foods. The proposed technique is validated by the successful detection of bacterial species from the contaminated surfaces. The use of a 3D nanostructure swab, portable Raman device, and deep learning-based classification provides a powerful tool for rapid identification (≈5 min) of foodborne bacterial species. The detection strategy shows significant potential for reliable food safety monitoring, making a meaningful contribution to public health and the food industry.