OBJECTIVE: Much has been written about the utility of genomic databases to public health. Within food safety these databases contain data from two types of isolates-those from patients (i.e., clinical) and those from non-clinical sources (e.g., a food manufacturing environment). A genetic match between isolates from these sources represents a signal of interest. We investigate the match rate within three large genomic databases (Listeria monocytogenes, Escherichia coli, and Salmonella) and the smaller Cronobacter database; the databases are part of the Pathogen Detection project at NCBI (National Center for Biotechnology Information).
RESULTS: Currently, the match rate of clinical isolates to non-clinical isolates is 33% for L. monocytogenes, 46% for Salmonella, and 7% for E. coli. These match rates are associated with several database features including the diversity of the organism, the database size, and the proportion of non-clinical BioSamples. Modeling match rate via logistic regression showed relatively good performance. Our prediction model illustrates the importance of populating databases with non-clinical isolates to better identify a match for clinical samples. Such information should help public health officials prioritize surveillance strategies and show the critical need to populate fledgling databases (e.g., Cronobacter sakazakii).

The wild boar, an impactful invasive species in Brazil, is subject to population control activities, which often include the use of hunting dogs. Hunters commonly consume wild boar meat, which is also used to feed their dogs, posing a risk of Toxoplasma gondii infection for humans and both T. gondii and Neospora caninum for dogs. The study aimed to investigate the prevalence of infection in wild boars (n = 127) and hunting dogs (n = 73) from São Paulo, Rio Grande do Sul, and Paraná states. We employed histopathological, serological (indirect fluorescent antibody test), and molecular techniques (endpoint polymerase chain reaction). Histopathology slides of wild boar tissue (central nervous system, heart, skeletal muscle, liver, spleen, kidney, gastrointestinal tract, pancreas, lymph nodes, and thyroid) sections revealed no T. gondii or N. caninum cysts (0/47). Antibodies anti-T. gondii were detected in 35/108 (32.4%) and anti-N. caninum in 45/108 (41.7%) wild boars. Only 2/18 (11.1%) wild boar tissue homogenate samples tested positive for T. gondii on endpoint PCR. Hunting dogs showed antibodies against T. gondii in 62/73 (85%) and against N. caninum in 31/73 (42%). The presence of antibodies against T. gondii and N. caninum in wild boars and hunting dogs, along with T. gondii DNA detection in wild boars, indicates the circulation of these parasites. Educating hunters on preventing these foodborne diseases, including zoonotic risks, is crucial.

Reducing foodborne disease incidence is a public health priority. This report summarizes preliminary 2023 Foodborne Diseases Active Surveillance Network (FoodNet) data and highlights efforts to increase the representativeness of FoodNet. During 2023, incidences of domestically acquired campylobacteriosis, Shiga toxin-producing Escherichia coli infection, yersiniosis, vibriosis, and cyclosporiasis increased, whereas those of listeriosis, salmonellosis, and shigellosis remained stable compared with incidences during 2016-2018, the baseline used for tracking progress towards federal disease reduction goals. During 2023, the incidence and percentage of infections diagnosed by culture-independent diagnostic tests (CIDTs) reported to FoodNet continued to increase, and the percentage of cases that yielded an isolate decreased, affecting observed trends in incidence. Because CIDTs allow for diagnosis of infections that previously would have gone undetected, lack of progress toward disease reduction goals might reflect changing diagnostic practices rather than an actual increase in incidence. Continued surveillance is needed to monitor the impact of changing diagnostic practices on disease trends, and targeted prevention efforts are needed to meet disease reduction goals. During 2023, FoodNet expanded its catchment area for the first time since 2004. This expansion improved the representativeness of the FoodNet catchment area, the ability of FoodNet to monitor trends in disease incidence, and the generalizability of FoodNet data.

CONCLUSIONS: The reliable and timely recognition of outbreaks is a key component of public health surveillance for foodborne diseases. Whole genome sequencing (WGS) offers high resolution typing of foodborne bacterial pathogens and facilitates the accurate detection of outbreaks. This detection relies on grouping WGS data into clusters at an appropriate genetic threshold. However, methods and tools for selecting and adjusting such thresholds according to the required resolution of surveillance and epidemiological context are lacking. Here we present DODGE (Dynamic Outbreak Detection for Genomic Epidemiology), an algorithm to dynamically select and compare these genetic thresholds. DODGE can analyse expanding datasets over time and clusters that are predicted to correspond to outbreaks (or \"investigation clusters\") can be named with established genomic nomenclature systems to facilitate integrated analysis across jurisdictions. DODGE was tested in two real-world Salmonella genomic surveillance datasets of different duration, 2 months from Australia and 9 years from the United Kingdom. In both cases only a minority of isolates were identified as investigation clusters. Two known outbreaks in the United Kingdom dataset were detected by DODGE and were recognized at an earlier timepoint than the outbreaks were reported. These findings demonstrated the potential of the DODGE approach to improve the effectiveness and timeliness of genomic surveillance for foodborne diseases and the effectiveness of the algorithm developed.
METHODS: DODGE is freely available at https://github.com/LanLab/dodge and can easily be installed using Conda.

UNASSIGNED: Foodborne diseases, representing significant food safety and public health challenges globally, are not well-documented in terms of incidence, particularly for cases characterized by acute gastroenteritis (AGI) in China.
UNASSIGNED: This study developed a pyramid model to estimate the incidence of five pathogens, stratified by gender and age. The estimated incidences per 100,000 people with 95% uncertainty intervals (UI) are as follows: Norovirus, 3,188.28 (95% UI: 2,518.03, 7,296.96); Salmonella spp., 1,295.59 (95% UI: 1,002.62, 1,573.11); diarrheagenic E. coli (DEC), 782.62 (95% UI: 651.19, 932.05); Vibrio parahaemolyticus, 404.06 (95% UI: 342.19, 468.93); and Shigella spp., 26.73 (95% UI: 21.05, 33.46).
UNASSIGNED: This study elucidates the incidence rates across various gender and age groups, thereby identifying priority populations for targeted preventive interventions aimed at reducing disease burden. These insights are crucial for the development of public health policies and management of food safety risks.

UNASSIGNED: Hospital refrigerators as essential food storage can be important source of food contamination. We aimed to investigate the frequency and antibiotic susceptibility of the pathogenic bacteria in three hospital refrigerators in Tehran.
UNASSIGNED: This study was performed on 254 samples, collected from 60 refrigerators of the various wards of three hospitals, A, B, and C, in Tehran, Iran from 2020 to 2021. Following isolation and identification of isolates, the antibiotic susceptibility pattern was determined. PCR-based assays were used to screen the presence of antibiotic resistance genes of resistant isolates.
UNASSIGNED: From 254 collected samples, 236 samples (92.9%) were contaminated. Most strains were isolated from refrigerators with poorly cleaned, temperatures above 8 °C in non-critical wards. Most bacteria belonging to Enterobacteriaceae (68.8%), followed by Staphylococcus (11.9%), and Enterococcus (10.6%), while the frequency of non-Enterobacteriaceae isolates was 8.9%. The highest antibiotic resistant bacteria were in extended spectrum beta-lactamase (ESBL) 9.7%, vancomycin-resistant enterococci (VRE) 5.3%, methicillin-resistant S. epidermidis (MRSE) 0.4%, and methicillin-resistant S. aureus (MRSA) 0.4%, respectively. The bla OXA-48, bla CTX, and bcla TEM genes were found only in 10% of Enterobacteriaceae isolates. The bla OXA-51 gene was found in all non-Enterobacteriaceae isolates. The vanA and mecA genes were detected in antibiotic-resistant Enterococcus and Staphylococcus.
UNASSIGNED: Our findings suggests major concern about cross-contamination and the emergence of antibiotic-resistant isolates as a potential health threat with hospital refrigerators origin. More attention to hospital refrigerators cleaning is necessary to prevent foodborne diseases and nosocomial infections.

Foodborne diseases can be attributed not only to contamination with bacterial or fungal pathogens but also their associated toxins. Thus, to maintain food safety, innovative decontamination techniques for toxins are required. We previously demonstrated that an atmospheric-pressure dielectric-barrier discharge (APDBD) plasma generated by a roller conveyer plasma device is effective at inactivating bacteria and fungi in foods. Here, we have further examined whether the roller conveyer plasma device can be used to degrade toxins produced by foodborne bacterial pathogens, including aflatoxin, Shiga toxins (Stx1 and Stx2), enterotoxin B and cereulide. Each toxin was spotted onto an aluminum plate, allowed to dry, and then treated with APDBD plasma applied by the roller conveyer plasma device for different time periods. Assessments were conducted using a competitive enzyme-linked immunosorbent assay (ELISA) and liquid chromatography-tandem mass spectrometry (LC-MS/MS). The results demonstrate a significant time-dependent decrease in the levels of these toxins. ELISA showed that aflatoxin B1 concentrations were reduced from 308.6 µg/mL to 74.4 µg/mL within 1 min. For Shiga toxins, Stx1 decreased from 913.8 µg/mL to 65.1 µg/mL, and Stx2 from 2309.0 µg/mL to 187.6 µg/mL within the same time frame (1 min). Enterotoxin B levels dropped from 62.67 µg/mL to 1.74 µg/mL at 15 min, and 1.43 µg/mL at 30 min, but did not display a significant decrease within 5 min. LC-MS/MS analysis verified that cereulide was reduced to below the detection limit following 30 min of APDBD plasma treatment. Taken together, these findings highlight that a range of foodborne toxins can be degraded by a relatively short exposure to plasma generated by an APDBD using a roller conveyer device. This technology offers promising advancements in food safety, providing a novel method to alleviate toxin contamination in the food processing industry.

Microbial foodborne pathogens present significant challenges to public health and the food industry, requiring rapid and accurate detection methods to prevent infections and ensure food safety. Conventional single biosensing techniques often exhibit limitations in terms of sensitivity, specificity, and rapidity. In response, there has been a growing interest in multimodal biosensing approaches that combine multiple sensing techniques to enhance the efficacy, accuracy, and precision in detecting these pathogens. This review investigates the current state of multimodal biosensing technologies and their potential applications within the food industry. Various multimodal biosensing platforms, such as opto-electrochemical, optical nanomaterial, multiple nanomaterial-based systems, hybrid biosensing microfluidics, and microfabrication techniques are discussed. The review provides an in-depth analysis of the advantages, challenges, and future prospects of multimodal biosensing for foodborne pathogens, emphasizing its transformative potential for food safety and public health. This comprehensive analysis aims to contribute to the development of innovative strategies for combating foodborne infections and ensuring the reliability of the global food supply chain.

Foodborne disease outbreaks due to bacterial pathogens and their toxins have become a serious concern for global public health and security. Finding novel antibacterial agents with unique mechanisms of action against the current spoilage and foodborne bacterial pathogens is a central strategy to overcome antibiotic resistance. This study examined the antibacterial activities and mechanisms of action of inorganic nanoparticles (NPs) against foodborne bacterial pathogens. The articles written in English were recovered from registers and databases (PubMed, ScienceDirect, Web of Science, Google Scholar, and Directory of Open Access Journals) and other sources (websites, organizations, and citation searching). \"Nanoparticles,\" \"Inorganic Nanoparticles,\" \"Metal Nanoparticles,\" \"Metal-Oxide Nanoparticles,\" \"Antimicrobial Activity,\" \"Antibacterial Activity,\" \"Foodborne Bacterial Pathogens,\" \"Mechanisms of Action,\" and \"Foodborne Diseases\" were the search terms used to retrieve the articles. The PRISMA-2020 checklist was applied for the article search strategy, article selection, data extraction, and result reporting for the review process. A total of 27 original research articles were included from a total of 3,575 articles obtained from the different search strategies. All studies demonstrated the antibacterial effectiveness of inorganic NPs and highlighted their different mechanisms of action against foodborne bacterial pathogens. In the present study, small-sized, spherical-shaped, engineered, capped, low-dissolution with water, high-concentration NPs, and in Gram-negative bacterial types had high antibacterial activity as compared to their counterparts. Cell wall interaction and membrane penetration, reactive oxygen species production, DNA damage, and protein synthesis inhibition were some of the generalized mechanisms recognized in the current study. Therefore, this study recommends the proper use of nontoxic inorganic nanoparticle products for food processing industries to ensure the quality and safety of food while minimizing antibiotic resistance among foodborne bacterial pathogens.

Salmonellosis, one of the most common foodborne infections in Europe, is monitored by food safety surveillance programmes, resulting in the generation of extensive databases. By leveraging tree-based machine learning (ML) algorithms, we exploited data from food safety audits to predict spatiotemporal patterns of salmonellosis in northwestern Italy. Data on human cases confirmed in 2015-2018 (n = 1969) and food surveillance data collected in 2014-2018 were used to develop ML algorithms. We integrated the monthly municipal human incidence with 27 potential predictors, including the observed prevalence of Salmonella in food. We applied the tree regression, random forest and gradient boosting algorithms considering different scenarios and evaluated their predictivity in terms of the mean absolute percentage error (MAPE) and R2. Using a similar dataset from the year 2019, spatiotemporal predictions and their relative sensitivities and specificities were obtained. Random forest and gradient boosting (R2 = 0.55, MAPE = 7.5%) outperformed the tree regression algorithm (R2 = 0.42, MAPE = 8.8%). Salmonella prevalence in food; spatial features; and monitoring efforts in ready-to-eat milk, fruits and vegetables, and pig meat products contributed the most to the models\' predictivity, reducing the variance by 90.5%. Conversely, the number of positive samples obtained for specific food matrices minimally influenced the predictions (2.9%). Spatiotemporal predictions for 2019 showed sensitivity and specificity levels of 46.5% (due to the lack of some infection hotspots) and 78.5%, respectively. This study demonstrates the added value of integrating data from human and veterinary health services to develop predictive models of human salmonellosis occurrence, providing early warnings useful for mitigating foodborne disease impacts on public health.