Using E. coli population to predict foodborne pathogens in pastured poultry farms

Microbiological safety assessment of restaurants and HACCP-certified kitchens in hotels: A study in eastern China

The Hazard Analysis and Critical Control Point (HACCP) system plays a crucial role in ensuring food safety within food service establishments, effectively reducing the risk of foodborne diseases. This study focused on assessing the risk of microbe contamination in poultry-based cook-served food during meal preparation in four restaurants and five selected HACCP-certified hotels in eastern China. We examined samples collected from 26 poultry-based cooked dishes, 248 food contact surfaces, 252 non-food contact surfaces, and 121 hand swabs. Our findings indicated a favorable trend of compliance with Chinese national standards, as Escherichia coli and Campylobacter were not detected in any cooked food samples. However, the microbiological assessments revealed non-compliance with total plate count standards in 7 % of the cooked samples from restaurants. In contrast, both dine-in hotels and restaurants exhibited significant non-compliance with guidance concerning food and non-food contact surfaces. Furthermore, our study found that chefs' hand hygiene did not meet microbiological reference standards, even after washing. Notably, Campylobacter persisted at 27 % and 30 % on chefs' hands, posing a significant risk of cross-contamination and foodborne diseases. These findings emphasize the urgent necessity for enhanced supervision of hygiene procedures and process monitoring in the HACCP-certified establishments engaged in the preparation and serving of food. Targeted interventions and food safety education for different chef subgroups can enhance food handling practices and reduce the risk of foodborne diseases in independent food establishments.

Prevalence of STEC virulence markers and Salmonella as a function of abiotic factors in agricultural water in the southeastern United States

Fresh produce can be contaminated by enteric pathogens throughout crop production, including through contact with contaminated agricultural water. The most common outbreaks and recalls in fresh produce are due to contamination by Salmonella enterica and Shiga toxin-producing E. coli (STEC). Thus, the objectives of this study were to investigate the prevalence of markers for STEC (wzy, hly, fliC, eaeA, rfbE, stx-I, stx-II) and Salmonella (invA) in surface water sources (n = 8) from produce farms in Southwest Georgia and to determine correlations among the prevalence of virulence markers for STEC, water nutrient profile, and environmental factors. Water samples (500 mL) from eight irrigation ponds were collected from February to December 2021 (n = 88). Polymerase chain reaction (PCR) was used to screen for Salmonella and STEC genes, and Salmonella samples were confirmed by culture-based methods. Positive samples for Salmonella were further serotyped. Particularly, Salmonella was detected in 6/88 (6.81%) water samples from all ponds, and the following 4 serotypes were detected: Saintpaul 3/6 (50%), Montevideo 1/6 (16.66%), Mississippi 1/6 (16.66%), and Bareilly 1/6 (16.66%). Salmonella isolates were only found in the summer months (May-Aug.). The most prevalent STEC genes were hly 77/88 (87.50%) and stx-I 75/88 (85.22%), followed by fliC 54/88 (61.63%), stx-II 41/88 (46.59%), rfbE 31/88 (35.22%), and eaeA 28/88 (31.81%). The wzy gene was not detected in any of the samples. Based on a logistic regression analysis, the odds of codetection for STEC virulence markers (stx-I, stx-II, and eaeA) were negatively correlated with calcium and relative humidity (p < 0.05). A conditional forest analysis was performed to assess predictive performance (AUC = 0.921), and the top predictors included humidity, nitrate, calcium, and solar radiation. Overall, information from this research adds to a growing body of knowledge regarding the risk that surface water sources pose to produce grown in subtropical environmental conditions and emphasizes the importance of understanding the use of abiotic factors as a holistic approach to understanding the microbial quality of water.

Determining how gavaging laying hens with lead (Pb) effects lead concentration in table eggs, bone, blood, and liver

Previous research has demonstrated that lead (Pb2+, for the remainder of the paper referred to as Pb) and other heavy metals are present in table eggs from backyard birds. While the source of Pb exposure is unknown, exposure to the environment via the foraging behavior of backyard poultry is likely a significant factor. A longitudinal cohort study was conducted to better understand how oral Pb consumption correlates with Pb concentration in table eggs and other physiological compartments (i.e. bone, blood, and liver). In order of absolute mean concentration, Pb was most present in bone (226.24 ppb experimental vs. 31.00 ppb control), blood (14.47 ppb experimental vs 0.27 ppb control), liver (11.24 ppb experimental vs 0.75 ppb control), and egg (1.17 ppb experimental vs 0.06 ppb control). The low concentration of Pb in table eggs implies that Pb preferentially accumulates in other tissue aside from the egg. While this study showed that eggs have the lowest overall tropism for Pb, the presence of Pb in other tissues demonstrates a public health risk when chickens are orally exposed to Pb via oral consumption.

Artificial Intelligence Models for Zoonotic Pathogens: A Survey

Zoonotic diseases or zoonoses are infections due to the natural transmission of pathogens between species (animals and humans). More than 70% of emerging infectious diseases are attributed to animal origin. Artificial Intelligence (AI) models have been used for studying zoonotic pathogens and the factors that contribute to their spread. The aim of this literature survey is to synthesize and analyze machine learning, and deep learning approaches applied to study zoonotic diseases to understand predictive models to help researchers identify the risk factors, and develop mitigation strategies. Based on our survey findings, machine learning and deep learning are commonly used for the prediction of both foodborne and zoonotic pathogens as well as the factors associated with the presence of the pathogens.