Use of ComBase data to develop an artificial neural network model for nonthermal inactivation of Campylobacter jejuni in milk and beef and evaluation of model performance and data completeness using the acceptable prediction zones method

Susceptibility of Campylobacter jejuni to Stressors in Agrifood Systems and Induction of a Viable-but-Nonculturable State

Many bacteria can become viable but nonculturable (VBNC) in response to stressors commonly identified in agrifood systems. Campylobacter is able to enter the VBNC state to evade unfavorable environmental conditions, but how food processing can induce Campylobacter jejuni to enter this state and the potential role of foods in inducing the VBNC state in C. jejuni remains largely unknown. In this study, the culturability and viability of C. jejuni cells were investigated under chlorine treatment (25 ppm), aerobic stress (atmospheric condition), and low-temperature (4°C) conditions that mimicked food processing. In addition, the behaviors of C. jejuni cells in ultrahigh-temperature (UHT) and pasteurized milk were also monitored during refrigerated storage. The numbers of viable and culturable C. jejuni cells in both the pure bacterial culture and food matrices were separately determined by propidium monoazide (PMA)-quantitative PCR (qPCR) and plating assay. The C. jejuni cells lost their culturability but partially retained their viability (1% to 10%) once mixed with chlorine. In comparison, ~10% of C. jejuni cells were induced to enter the VBNC state after 24 h and 20 days under aerobic and low-temperature conditions, respectively. The viability of the C. jejuni cells remained stable during the induction process in UHT (>10%) and pasteurized (>10%) milk. The number of culturable C. jejuni cells decreased quickly in pasteurized milk, but culturable cells could still be detected in the end (day 21). In contrast, the number of culturable C. jejuni cells slowly decreased, and they became undetectable after >42 days in UHT milk. The C. jejuni cells responded differently to various stress conditions and survived in high numbers in the VBNC state in agrifood systems. IMPORTANCE The VBNC state of pathogens can pose risks to food safety and public health because the pathogens cannot be detected using conventional microbiological culture-based methods but can resuscitate under favorable conditions to develop virulence. As a leading cause of human gastroenteritis worldwide, C. jejuni can enter the VBNC state to survive in the environment and food-processing chain with high prevalence. In this study, the effect of food-processing conditions and food products on the development of VBNC state in C. jejuni was investigated, providing a better understanding of the interaction between C. jejuni and the agroecosystem. The knowledge elicited from this study can aid in developing novel intervention strategies to reduce the food safety risks associated with this microbe.

Longitudinal Study for the Detection and Quantification of Campylobacter spp. in Dairy Cows during Milking and in the Dairy Farm Environment

Campylobacteriosis outbreaks have repeatedly been associated with the consumption of raw milk. This study aimed to explore the variation in the prevalence and concentration of Campylobacter spp. in cows' milk and feces, the farm environment and on the teat skin over an entire year at a small German dairy farm. Bi-weekly samples were collected from the environment (boot socks), teats, raw milk, milk filters, milking clusters and feces collected from the recta of dairy cows. Samples were analyzed for Campylobacter spp., E. coli, the total aerobic plate count and for Pseudomonas spp. The prevalence of Campylobacter spp. was found to be the highest in feces (77.1%), completely absent in milking equipment and low in raw milk (0.4%). The mean concentration of Campylobacter spp. was 2.43 log₁₀ colony-forming units (CFU)/g in feces and 1.26 log₁₀ CFU/teat swab. Only a single milk filter at the end of the milk pipeline and one individual cow's raw milk sample were positive on the same day, with a concentration of 2.74 log₁₀ CFU/filter and 2.37 log₁₀ CFU/mL for the raw milk. On the same day, nine teat swab samples tested positive for Campylobacter spp. This study highlights the persistence of Campylobacter spp. for at least one year in the intestine of individual cows and within the general farm environment and demonstrates that fecal cross-contamination of the teats can occur even when the contamination of raw milk is a rare event.

Food safety and food security through predictive microbiology tools: a short review

This article discusses the issues of food safety and food security as a matter of global health. Foodborne illness and deaths caused by pathogens in food continue to be a worldwide problem, with a reported 600 million cases per year, leading to around 420,000 deaths in 2010. Predictive microbiology can play a crucial role in ensuring safe food through mathematical modelling to estimate microbial growth and behaviour. Food security is described as the social and economical means of accessing safe and nutritious food that meets people's dietary preferences and requirements for an active and healthy life. The article also examines various factors that influence food security, including economic, environmental, technological, and geopolitical challenges globally. The concept of food safety is described as a science-based process or action that prevents food from containing substances that could harm human health. Food safety receives limited attention from policymakers and consumers in low- and middle-income countries, where food safety issues are most prevalent. The article also highlights the importance of detecting contaminants and pathogens in food to prevent foodborne illnesses and reduce food waste. Food and Agriculture Organization (FAO), an institution belonging to World Health Organization (WHO) presented calls to action to solve some of the emerging problems in food safety, as it should be a concern of all people to be involved in the pursue of safer food. The guarantee of safe food pertaining to microbiological contamination, as there are different types of active microorganisms in foods, could be obtained using predictive microbiology tools, which study and analyse different microorganisms' behaviour through mathematical models. Studies published by several authors show the application of primary, secondary, or tertiary models of predictive microbiology used for different food products.

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.