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Campylobacter has consistently posed a food safety issue in broiler meat. This study aimed to create a quantitative microbial risk assessment model from retail to consumption, designed to evaluate the seasonal risk of campylobacteriosis associated with broiler meat consumption in the United States. To achieve this, data was gathered to build distributions that would enable us to predict the growth of Campylobacter during various stages such as retail storage, transit, and home storage. The model also included potential fluctuations in concentration during food preparation and potential cross-contamination scenarios. A Monte Carlo simulation with 100,000 iterations was used to estimate the risk of infection per serving and the number of infections in the United States by season. In the summer, chicken meat was estimated to have a median risk of infection per serving of 9.22 × 10−7 and cause an average of about 27,058,680 infections. During the winter months, the median risk of infection per serving was estimated to be 4.06 × 10−7 and cause an average of about 12,085,638 infections. The risk assessment model provides information about the risk of broiler meat to public health by season. These results will help understand the most important steps to reduce the food safety risks from contaminated chicken products. [ABSTRACT FROM AUTHOR]

Contamination of poultry products by Campylobacter is often associated with farm management practices and processing plant practices. A longitudinal study was conducted on 11 pastured poultry farms in southeastern United States from 2014 to 2017. In this study, farm practices and processing variables were used as predictors for a random forest (RF) model to predict Campylobacter prevalence in pastured poultry farms and processing environments. Individual RF models were constructed for fecal, soil and whole carcass rinse after processing (WCR-P) samples. The performance of models was evaluated by the area under curve (AUC) from the receiver operating characteristics curve. The AUC values were 0.902, 0.894, and 0.864 for fecal, soil, and WCR-P models, respectively. Relative importance plots were generated to predict the most important variable in each RF model. Animal source of feces was identified as the most important variable in fecal model and the soy content of the brood feed was the most important variable for soil model. For WCR-P model, the average flock age showed the strongest impact on RF model. These RF models can help pastured poultry growers with food safety control strategies to reduce Campylobacter prevalence in pastured poultry farms. [ABSTRACT FROM AUTHOR]

Purpose: We previously reported an inverse association between flavonoid intake and breast cancer incidence, which has been confirmed by others, but no studies have considered simultaneously potential interactions of flavonoids with multiple genetic polymorphisms involved in biologically relevant pathways (oxidative stress, carcinogen metabolism, DNA repair, and one-carbon metabolism)., Methods: To estimate interaction effects between flavonoids and 13 polymorphisms in these four pathways on breast cancer risk, we used population-based data (n = 875 cases and 903 controls) and several statistical approaches, including conventional logistic regression and semi-Bayesian hierarchical modeling (incorporating prior information on the possible biologic functions of genes), which also provides biologic pathway-specific effect estimates., Results: Compared to the standard multivariate model, the results from the hierarchical model indicate that gene-by-flavonoid interaction estimates are attenuated, but more precise. In the hierarchical model, the average effect of the deleterious versus beneficial gene, controlling for average flavonoid intake in the DNA repair pathway, and adjusted for the three other biologically relevant pathways (oxidative stress, carcinogen metabolism, and one-carbon metabolism), resulted in a 27 % increase risk for breast cancer [odds ratio = 1.27; 95 % confidence interval (CI) = 0.70, 2.29]. However, the CI was wide., Conclusions: Based on results from the semi-Bayesian model, breast cancer risk may be influenced jointly by flavonoid intake and genes involved in DNA repair, but our findings require confirmation.