The objective of this study was to examine changes in healthcare-seeking behaviors and diagnostic practices around foodborne illness during the COVID-19 pandemic in a large university-based health system. A retrospective cohort study of individuals diagnosed with pathogens commonly transmitted through food between 2015 and 2020 was undertaken using electronic medical record data. Regression models were used to compare measured incidence rates of various foodborne pathogens as well as associated healthcare-seeking behaviors during the pandemic year of 2020 to previous years. Incidence of campylobacteriosis, cholera, and norovirus in 2020 significantly decreased, respectively, by 65.5% (p < 0.01), 90.1% (p = 0.02), and 73.0% (p = 0.03) compared with an average from 2017- to 019. Average annual visits for patients included in our sample significantly increased by 8.0% when comparing the average from 2017-2019 to 2020 (p < 0.01). These results suggest that the pandemic impacted healthcare use related to foodborne disease either due to reduced exposure to foodborne pathogens or reduced willingness to seek healthcare.

UNASSIGNED: Nontyphoidal Salmonella infection is one of the most common foodborne illnesses, and its oncogenic potential has been documented in animal models. The primary goal of this study was to examine whether individuals who were exposed to enteric Salmonella infection are more likely to develop colorectal cancer (CRC) than the general population through the linkage of 2 statewide public health surveillance databases.
UNASSIGNED: We designed a 2-stage probabilistic linkage, starting with 17,587 records of enteric salmonellosis reported to Michigan Department of Health and Human Services between 1992 and 2020. These records did not include unique identifiers (such as Social Security number [SSN]). The initial linkage to LexisNexis address history was conducted to obtain information to calculate each person\'s time in Michigan as well as SSN for the second linkage. The linkage to the state cancer registry was performed to obtain the observed number of CRC cases, while the expected number of CRC cases was calculated according to corresponding state CRC incidence by age, sex, and calendar year.
UNASSIGNED: Ninety-three percent of the initially identified salmonellosis records were sent to LexisNexis linkage, which returned address history, death, and SSN for 97% of the records. Further linkage to the statewide cancer registry identified 98 incident CRC cases. Overall, the observed-to-expected (O/E) ratio was not different from unity (0.833; 95% CI, 0.627-1.003).
UNASSIGNED: While the new linkage strategy was found effective and should be applicable to other health conditions, we cannot rule out bias due to incomplete or underreporting of the infection in estimating the risk of CRC.

Clostridium perfringens (CP) is a common cause of foodborne infection, leading to significant human health risks and a high economic burden. Thus, effective CP disease surveillance is essential for preventive and therapeutic interventions; however, conventional practices often entail complex, resource-intensive, and costly procedures. This study introduced a data-driven machine learning (ML) modeling framework for CP-related disease surveillance. It leveraged an integrated dataset of municipal wastewater microbiome (e.g., CP abundance), crowdsourced (CP-related web search keywords), and environmental data. Various optimization strategies, including data integration, data normalization, model selection, and hyperparameter tuning, were implemented to improve the ML modeling performance, leading to enhanced predictions of CP cases over time. Explainable artificial intelligence methods identified CP abundance as the most reliable predictor of CP disease cases. Multi-omics subsequently revealed the presence of CP and its genotypes/toxinotypes in wastewater, validating the utility of microbiome-data-enabled ML surveillance for foodborne diseases. This ML-based framework thus exhibits significant potential for complementing and reinforcing existing disease surveillance systems.

Between 2017 and 2019, pulsed-field gel electrophoresis was replaced by whole genome sequencing (WGS) for identifying enteric disease clusters in Canada. The number and characteristics of all clusters of Listeria monocytogenes, Salmonella, Shiga toxin-producing Escherichia coli (STEC), and Shigella spp. between 2015 and 2021 were analyzed. Following the transition to WGS, an increase in the number of Salmonella, STEC, and Shigella clusters was noted, whereas the number of clusters of L. monocytogenes decreased. Unlike previous subtyping methods, WGS provided increased resolution to identify discrete clusters of Salmonella Enteritidis. This led to the identification of a number of outbreaks linked to frozen raw breaded chicken products and ultimately a change in food safety policy to reduce the number of illnesses associated with these products. Other pathogens did not experience a similar increase in the number of outbreaks detected. Although WGS did provide increased confidence in the genetic relatedness of cases and isolates, challenges remained in collecting epidemiological data to link these illnesses to a common source.

Integration of machine learning (ML) technologies into the realm of smart food safety represents a rapidly evolving field with significant potential to transform the management and assurance of food quality and safety. This chapter will discuss the capabilities of ML across different segments of the food supply chain, encompassing pre-harvest agricultural activities to post-harvest processes and delivery to the consumers. Three specific examples of applying cutting-edge ML to advance food science are detailed in this chapter, including its use to improve beer flavor, using natural language processing to predict food safety incidents, and leveraging social media to detect foodborne disease outbreaks. Despite advances in both theory and practice, application of ML to smart food safety still suffers from issues such as data availability, model reliability, and transparency. Solving these problems can help realize the full potential of ML in food safety. Development of ML in smart food safety is also driven by social and industry impacts. The improvement and implementation of legal policies brings both opportunities and challenges. The future of smart food safety lies in the strategic implementation of ML technologies, navigating social and industry impacts, and adapting to regulatory changes in the AI era.

Foodborne illnesses caused by consuming contaminated fresh produce not only pose serious public health risks but also lead to huge economic losses. Rockmelons (cantaloupes) have emerged as a recurrent source of disease outbreaks caused by foodborne pathogens, including Listeria monocytogenes, Salmonella, and Escherichia coli. The most common factor of the outbreaks was the microbial contamination of rockmelons at the farm, and subsequently, the pathogenic bacteria were transferred to the flesh during cutting and processing. One of the deadliest outbreaks occurred in the USA due to L. monocytogenes contamination of rockmelons which caused 33 deaths in 2011. Since then, several guidelines and recommendations have been developed for food safety management to reduce the microbial contamination of melons on farms and post-harvest operations. This article explicitly provides an updated overview of microbiological contamination, disease outbreaks, pathogens prevalence, and mitigation strategies to reduce public health risks due to the consumption of rockmelons.

Listeria monocytogenes is a Gram-positive facultative anaerobic bacterium that is ubiquitous in the environment and can cause severe infections in immunocompromised individuals, pregnant women, and newborns. Listeriosis can manifest as meningitis, encephalitis, or sepsis, and its diagnosis requires a high index of suspicion. The case is reported of a rare presentation of rhombencephalitis by listeriosis in a 61-year-old male who initially suffered from subacute gastric disturbances and fever. Neurological consultation showed abnormal functions of cranial nerves and meningeal signs were observed. MRI revealed a poorly demarcated focus of approximately 45 × 16 × 15mm, indicating possible inflammatory processes, necessitating a lumbar puncture. Assessment of the CSF indicated infection with the bacterium- Listeria Monocytogenes, with the final diagnosis of Listeriosis encephalitis. Despite antibiotic therapy of Ceftazidine and Ampicillin, the patient\'s condition deteriorated, followed by death.

UNASSIGNED: This research aimed to analyze the prevalence, molecular characteristics, toxinotyping, alpha toxin production potential, and antibiotic resistance pattern of Clostridium perfringens (C. perfringens) isolates in meat samples collected from various sources.
UNASSIGNED: Sixty meat samples were screened for alpha toxin using Enzyme-Linked Immunosorbent Assay (ELISA), revealing a positivity rate of 13.3%, predominantly in raw poultry meat. Subsequent culturing on Perfringens agar identified nine samples harboring characteristic C. perfringens colonies, primarily isolated from raw poultry meat. Molecular confirmation through 16S rRNA gene amplification and sequencing authenticated twelve isolates as C. perfringens, with nine strains exhibiting genetic resemblance to locally isolated strains. Toxinotyping assays targeting alpha toxin-specific genes confirmed all nine isolates as type A C. perfringens, with no detection of beta or epsilon toxin genes. Hemolytic assays demonstrated varying alpha toxin production potentials among isolates, with accession number OQ721004.1 displaying the highest production capacity. Moreover, antibiotic resistance profiling revealed multi-drug resistance patterns among the isolates.
UNASSIGNED: The study identified distinct clusters within C. perfringens strains, indicating variations. Phylogenetic analysis delineated genetic relatedness among strains, elucidating potential evolutionary paths and divergences.
UNASSIGNED: The findings underscore the need for robust surveillance and control measures to mitigate the risk of C. perfringens contamination in meat products, particularly in raw poultry meat. Enhanced monitoring and prudent antimicrobial stewardship practices are warranted in both veterinary and clinical settings to address the observed antibiotic resistance profiles and prevent foodborne outbreaks.

Foodborne illnesses caused by the consumption of contaminated foods have frequent occurrences in developing countries. The incorporation of contaminated water in food processes, preparation, and serving is directly linked to several gastrointestinal infections. Keeping in view, this study was conducted to assess the microbial quality of both drinking water sources and commonly consumed fresh ready-to-eat (RTE) foods in the region. The drinking water samples from water sources and consumer points, as well as food samples from canteens, cafes, hotels, and restaurants, were collected for the microbiological analysis. Fifty-five percent (n = 286) of water samples were found to be positive for total coliforms with MPN counts ranging from 3 to 2600 (100 ml) -1. E. coli was detected in nearly 30% of the total water samples. Overall, 65% tap water samples were found unsatisfactory, followed by submersible (53%), filter (40%), and WTP (30%) sources. Furthermore, the examination of RTE foods (n = 80) found that 60% were of unsatisfactory microbial quality with high aerobic plate counts. The salads were the most contaminated category with highest mean APC 8.3 log CFU/g followed by pani puri, chats, and chutneys. Presence of coliforms and common enteropathogens was observed in both water and food samples. The detected isolates from the samples were identified as Enterobacter spp., Klebsiella spp., Pseudomonas aeruginosa, Salmonella spp., Shigella spp., and Staphylococcus spp. Based on these findings, microbiological quality was found compromised and this may pose hazard to public health. This exploratory study in the Punjab region also suggests that poor microbiological quality of water sources can be an important source of contamination for fresh uncooked RTE foods, thus transferring pathogens to the food chain. Therefore, only safe potable drinking water post-treatment should be used at all stages.

Vibrio parahaemolyticus is an important human pathogen that is currently the leading cause of shellfish-borne gastroenteritis in the world. Particularly, the pandemic strain has the capacity to induce cytotoxicity and enterotoxicity through its Type 3 Secretion System (T3SS2) that leads to massive cell death. However, the specific mechanism by which the T3SS2 induces cell death remains unclear and its contribution to mitochondrial stress is not fully understood. In this work, we evaluated the contribution of the T3SS2 of V. parahaemolyticus in generating mitochondrial stress during infection in human intestinal HT-29 cells. To evaluate the contribution of the T3SS2 of V. parahaemolyticus in mitochondrial stress, infection assays were carried out to evaluate mitochondrial transition pore opening, mitochondrial fragmentation, ATP quantification, and cell viability during infection. Our results showed that the Δvscn1 (T3SS2+) mutant strain contributes to generating the sustained opening of the mitochondrial transition pore. Furthermore, it generates perturbations in the ATP production in infected cells, leading to a significant decrease in cell viability and loss of membrane integrity. Our results suggest that the T3SS2 from V. parahaemolyticus plays a role in generating mitochondrial stress that leads to cell death in human intestinal HT-29 cells. It is important to highlight that this study represents the first report indicating the possible role of the V. parahaemolyticus T3SS2 and its effector proteins involvement in generating mitochondrial stress, its impact on the mitochondrial pore, and its effect on ATP production in human cells.