Reported Incidence of Infections Caused by Pathogens Transmitted Commonly Through Food: Impact of Increased Use of Culture-Independent Diagnostic Tests - Foodborne Diseases Active Surveillance Network, 1996-2023.

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.

DODGE: automated point source bacterial outbreak detection using cumulative long term genomic surveillance.

SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: DODGE is freely available at https://github.com/LanLab/dodge and can easily be installed using Conda.

Prospective modeling and estimating the epidemiologically informative match rate within large foodborne pathogen genomic databases.

OBJECTIVES: 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).

[Development of a Genus Identification Method for Poisonous Plants Using Real-Time PCR].

We have developed a rapid genus identification method for poisonous plants. The real-time PCR using the TaqMan® probe method was employed for detection, with the amplified targets being the "trnL (UAA)-intron" or "trnL-trnF intergenic spacer" regions of chloroplast DNA. The targeted plants were selected six genera (Aconitum, Colchicum, Veratrum, Brugmansia, Scopolia and Narcissus), which have been implicated in many instances of food poisoning in Japan. A tissue lysis solution was used for DNA extraction, which can be completed within approximate 30 min. A master mix corresponding to the tissue lysis solution was used for real-time PCR reagents. As a result, we were able to complete the entire process from DNA extraction to genus identification in 4 to 5 hr. The detection sensitivity was estimated at approximately 1 pg of DNA for all six plant genera. Remarkably, an amplification plot was discerned even with the crude cell lysates of all samples. It was also possible to obtain amplification curves for three plant samples that had been subjected to simulated cooking (boiling). This study suggests that the developed method can rapidly identify six genera of poisonous plants.

Foodborne bacteria in milk and milk products along the water buffalo milk chain in Bangladesh.

Controlling foodborne pathogens in buffalo milk is crucial for ensuring food safety. This study estimated the prevalence of nine target genes representing seven critical foodborne bacteria in milk and milk products, and identified factors associated with their presence in buffalo milk chain nodes in Bangladesh. One hundred and forty-three milk samples from bulk tank milk (n = 34), middlemen (n = 37), milk collection centers (n = 37), and milk product shops (n = 35) were collected and analyzed using RT-PCR. Escherichia (E.) coli, represented through yccT genes, was the most prevalent throughout the milk chain (81-97%). Chi-squared tests were performed to identify the potential risk factors associated with the presence of foodborne bacteria encoded for different genes. At the middleman level, the prevalence of E. coli was associated with the Mymensingh, Noakhali, and Bhola districts (P = 0.01). The prevalence of Listeria monocytogenes, represented through inlA genes, and Yersinia (Y.) enterocolitica, represented through yst genes, were the highest at the farm level (65-79%). The prevalence of both bacteria in bulk milk was associated with the Noakhali and Bhola districts (P < 0.05). The prevalence of Y. enterocolitica in bulk milk was also associated with late autumn and spring (P = 0.01) and was higher in buffalo-cow mixed milk than in pure buffalo milk at the milk collection center level (P < 0.01). The gene stx2 encoding for Shiga toxin-producing (STEC) E. coli was detected in 74% of the milk products. At the middleman level, the prevalence of STEC E. coli was associated with the use of cloths or tissues when drying milk containers (P = 0.01). Salmonella enterica, represented through the presence of invA gene, was most commonly detected (14%) at the milk collection center. The use of plastic milk containers was associated with a higher prevalence of Staphylococcus aureus, represented through htrA genes, at milk product shops (P < 0.05). These results suggest that raw milk consumers in Bangladesh are at risk if they purchase and consume unpasteurized milk.

Detection of foodborne pathogens in contaminated food using nanomaterial-based electrochemical biosensors.

Foodborne pathogens are a grave concern for the for food, medical, environmental, and economic sectors. Their ease of transmission and resistance to treatments, such as antimicrobial agents, make them an important challenge. Food tainted with these pathogens is swiftly rejected, and if ingested, can result in severe illnesses and even fatalities. This review provides and overview of the current status of various pathogens and their metabolites transmitted through food. Despite a plethora of studies on treatments to eradicate and inhibit these pathogens, their indiscriminate use can compromise the sensory properties of food and lead to contamination. Therefore, the study of detection methods such as electrochemical biosensors has been proposed, which are devices with advantages such as simplicity, fast response, and sensitivity. However, these biosensors may also present some limitations. In this regard, it has been reported that nanomaterials with high conductivity, surface-to-volume ratio, and robustness have been observed to improve the detection of foodborne pathogens or their metabolites. Therefore, in this work, we analyze the detection of pathogens transmitted through food and their metabolites using electrochemical biosensors based on nanomaterials.

Foodborne pathogenic bacteria in wild European hedgehogs (Erinaceus europaeus).

BACKGROUND: European hedgehogs (Erinaceus europaeus) are widely distributed across Europe. They may play an important role by spreading zoonotic bacteria in the environment and to humans and animals. The aim of our work was to study the prevalence and characteristics of the most important foodborne bacterial pathogens in wild hedgehogs. RESULTS: Faecal samples from 148 hospitalised wild hedgehogs originating from the Helsinki region in southern Finland were studied. Foodborne pathogens were detected in 60% of the hedgehogs by PCR. Listeria (26%) and STEC (26%) were the most common foodborne pathogens. Salmonella, Yersinia, and Campylobacter were detected in 18%, 16%, and 7% of hedgehogs, respectively. Salmonella and Yersinia were highly susceptible to the tested antimicrobials. Salmonella Enteritidis and Listeria monocytogenes 2a were the most common types found in hedgehogs. All S. Enteritidis belonged to one sequence type (ST11), forming four clusters of closely related isolates. L. monocytogenes was genetically more diverse than Salmonella, belonging to 11 STs. C. jejuni ST45 and ST677, Y. pseudotuberculosis O:1 of ST9 and ST42, and Y. enterocolitica O:9 of ST139 were also found. CONCLUSIONS: Our study shows that wild European hedgehogs should be considered an important source of foodborne pathogens, and appropriate hygiene measures after any contact with hedgehogs and strict biosecurity around farms are therefore important.

Comprehensive analysis of predominant pathogenic bacteria and viruses in seafood products.

Given the growing global demand for seafood, it is imperative to conduct a comprehensive study on the prevalence and persistence patterns of pathogenic bacteria and viruses associated with specific seafood varieties. This assessment thoroughly examines the safety of seafood products, considering the diverse processing methods employed in the industry. The importance of understanding the behavior of foodborne pathogens, such as Salmonella typhimurium, Vibrio parahaemolyticus, Clostridium botulinum, Listeria monocytogenes, human norovirus, and hepatitis A virus, is emphasized by recent cases of gastroenteritis outbreaks linked to contaminated seafood. This analysis examines outbreaks linked to seafood in the United States and globally, with a particular emphasis on the health concerns posed by pathogenic bacteria and viruses to consumers. Ensuring the safety of seafood is crucial since it directly relates to consumer preferences on sustainability, food safety, provenance, and availability. The review focuses on assessing the frequency, growth, and durability of infections that arise during the processing of seafood. It utilizes next-generation sequencing to identify the bacteria responsible for these illnesses. Additionally, it analyzes methods for preventing and intervening of infections while also considering the forthcoming challenges in ensuring the microbiological safety of seafood products. This evaluation emphasizes the significance of the seafood processing industry in promptly responding to evolving consumer preferences by offering current information on seafood hazards and future consumption patterns. To ensure the continuous safety and sustainable future of seafood products, it is crucial to identify and address possible threats.

Multimodal Biosensing of Foodborne Pathogens.

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.

Degradation of Toxins Derived from Foodborne Pathogens by Atmospheric-Pressure Dielectric-Barrier Discharge.

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.

Interference of gastrointestinal barriers with antibiotic susceptibility of foodborne pathogens: an in vitro case study of ciprofloxacin and tetracycline against Salmonella enterica and Listeria monocytogenes.

Minimum inhibitory concentrations (MIC) assays are often questioned for their representativeness. Especially when foodborne pathogens are tested, it is of crucial importance to also consider parameters of the human digestive system. Hence, the current study aimed to assess the inhibitory capacity of two antibiotics, ciprofloxacin and tetracycline, against Salmonella enterica and Listeria monocytogenes, under representative environmental conditions. More specifically, aspects of the harsh environment of the human gastrointestinal tract (GIT) were gradually added to the experimental conditions starting from simple aerobic lab conditions into an in vitro simulation of the GIT. In this way, the effects of parameters including the anoxic environment, physicochemical conditions of the GIT (low gastric pH, digestive enzymes, bile acids) and the gut microbiota were evaluated. The latter was simulated by including a representative consortium of selected gut bacteria species. In this study, the MIC of the two antibiotics against the relevant foodborne pathogens were established, under the previously mentioned environmental conditions. The results of S. enterica highlighted the importance of the anaerobic environment when conducting such studies, since the pathogen thrived under such conditions. Inclusion of physicochemical barriers led to exactly opposite results for S. enterica and L. monocytogenes since the former became more susceptible to ciprofloxacin while the latter showed lower susceptibility towards tetracycline. Finally, the inclusion of gut bacteria had a bactericidal effect against L. monocytogenes even in the absence of antibiotics, while gut bacteria protected S. enterica from the effect of ciprofloxacin.

[A fish-intoxication as a rare cause of chest pain]. / Eine Fischintoxikation als seltene Ätiologie des Thoraxschmerzes.

INTRODUCTION: We describe the case of a 58-year-old patient who developed chest pain and an anaphylaktoide reaction after ingestion of contamined fish containing histamin. Histamin intoxication from food poisoning (also known as scombroid intoxication) can be mistaken for an anaphylactic reaction and occasionaly lead to cardiac symptoms, even in patients without atherosclerotic changes. This condition is called Kounis syndrom and has to be recognized as a separate syndrom with specific clinical features.

Machine learning approach as an early warning system to prevent foodborne Salmonella outbreaks in northwestern Italy.

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.

Prevalence and characterization of foodborne pathogens isolated from fresh-cut fruits and vegetables in Beijing, China.

Pre-cut fresh fruits and vegetables are highly appealing to consumers for their convenience, however, as they are highly susceptible to microbial contamination in processing, the potential risks of foodborne illnesses to public health are not negligible. This study aimed to assess the prevalence, antibiotic susceptibility and molecular characteristics of major foodborne pathogens (Listeria monocytogenes, Escherichia coli, Staphylococcus aureus and Salmonella) isolated from fresh-cut fruits and vegetables in Beijing, China. 86 stains were isolated from 326 samples, with S. aureus being the highest prevalence (15.38 %), followed by E. coli (9.23 %) and L. monocytogenes (1.85 %), while no Salmonella was detected. The prevalence by type of food indicated that fruit trays and mixed vegetables were more susceptible to contamination by pathogens. 98 % of S. aureus were resistant to at least of one antibiotic, and showed a high resistance rate to benzylpenicillin (90 %) and oxacillin (48 %). Among 25 E. coli isolates, 57.67 % of which exhibited multi-drug resistance, with common resist to trimethoprim/sulfamethoxazole (66.67 %) and ampicillin (63.33 %). A total of 9 sequence types (STs) and 8 spa types were identified in 35 S. aureus isolates, with ST398-t34 being the predominant type (42.86 %). Additionally, analysis of 25 E. coli isolates demonstrated significant heterogeneity, characterized by 22 serotypes and 18 STs. Genomic analysis revealed that 5 and 44 distinct antibiotic resistance genes (ARGs) in S. aureus and E. coli, respectively. Seven quinolone resistance-determining regions (QRDRs) mutations were identified in E. coli isolates, of which GyrA (S83L) was the most frequently detected. All the S. aureus and E. coli isolates harbored virulence genes. ARGs in S. aureus and E. coli showed a significant positive correlation with plasmids. Furthermore, one L. monocytogenes isolate, which was ST101 and serogroupIIc from watermelon sample, harbored virulence genes (inlA and inlB) and LIPI-1 pathogenic islands (prfA, plcA, hly and actA), which posed potential risks for consumer's health. This study focused on the potential microbial risk of fresh-cut fruits and vegetables associated with foodborne diseases, improving the scientific understanding towards risk assessment related to ready-to-eat foods.

How heatwaves affect short-term emergency hospital admissions due to bacterial foodborne diseases.

The coming decades are likely to see of extreme weather events becoming more intense and frequent across Europe as a whole and around the Mediterranean in particular. The reproduction rate of some microorganisms, including the bacteria that cause foodborne diseases, will also be affected by these events. The aim of this study was thus to ascertain whether there might be a statistically significant relationship between emergency hospital admissions due to the principal bacterial foodborne diseases (BFDs) and the various meteorological variables, including heatwaves. We conducted a time-series study, with daily observations of both the dependent variable (emergency hospital admissions due to BFDs) and the independent variables (meteorological variables and control variables of chemical air pollution) across the period 2013-2018 in the Madrid Region (Spain), using Generalised Linear Models with Poisson regression, in which control and lag variables were included for the purpose of fitting the models. We calculated the threshold value of the maximum daily temperature above which such admissions increased statistically significantly, analysed data for the whole year and for the summer months alone, and estimated the relative and attributable risks. The estimated attributable risk was 3.6 % for every one-degree rise in the maximum daily temperature above 12 °C throughout the year, and 12.21 % for every one degree rise in temperature above the threshold heatwave definition temperature (34 °C) in summer. Furthermore, different meteorological variables displayed a statistically significant association. Whereas hours of sunlight and mean wind speed proved significant in the analyses of both the whole year and summer, the variables "rain" and "relative humidity", only showed a significant relationship in the analysis for the whole year. High ambient temperature is a risk factor that favours the increase in emergency hospitalisations attributable to the principal BFDs, with a greater impact being observed on days coinciding with heatwave periods. The results yielded by this study could serve as a basis for implementing BFD prevention strategies, especially on heatwave days.

Detection of Foodborne RNA Viruses by Reverse Transcriptase Droplet Digital PCR.

Foodborne viruses remain the largest cause of human gastroenteritis and one of the largest contributors to foodborne illnesses worldwide. Currently, quantitative reverse transcription PCR (qRT-PCR) or real-time qPCR are the detection methods commonly used for quantification of foodborne viruses, but those methods have several disadvantages, such as relying on standard curves for quantification and the background noise from a bulk reaction. ddPCR uses an oil-water emulsion to form multiple droplets that partition small amounts of viral genetic material (DNA or RNA) into each of the droplets. These droplets then undergo amplification cycles and are analyzed using Poisson distributions. This allows for absolute quantification without the need for a standard curve, which makes ddPCR a precise tool in surveillance of foodborne viruses. Herein, we describe the process of detecting foodborne viruses using RNA isolated from various matrices. Up to 96 samples including the positive and negative controls can be analyzed on a single plate by ddPCR.

Antibacterial Activity and Mechanisms of Action of Inorganic Nanoparticles against Foodborne Bacterial Pathogens: A Systematic Review.

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.