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ABSTRACT: Salmonella and Campylobacter are common bacterial hazards causing foodborne illnesses worldwide. A large proportion of Salmonella and Campylobacter illnesses are attributed to contaminated poultry products that are mishandled or under cooked. Processing interventions such as chilling and post-chill dip are critical to reducing microbial contamination of poultry. A comprehensive search of the literature published between 2000 and 2021 was conducted in the databases Web of Science, Academic Search Complete, and Academic OneFile. Studies were included if they were in English and investigated the effects of interventions against Salmonella and/or Campylobacter on whole carcasses and/or parts during the chilling or post-chill stages of poultry processing. Random-effects meta-analyses were performed using the “meta” package in the R programming language. Subgroup analyses were assessed according to outcome measure reported, microorganism tested, processing stage assessed, and chemical treatment used. The results included 41 eligible studies. Eighteen studies reported results of 28 separate interventions against Salmonella and 31 reported results of 50 separate interventions against Campylobacter. No significant difference (P> 0.05) was observed when comparing the combined mean difference of all interventions targeting Salmonella to the combined mean difference of all interventions targeting Campylobacter or when comparing chilling times within each pathogen subgroup. For analyses examining antimicrobial additives, peroxyacetic acid (PAA) had the largest reduction against Salmonella population regardless of chilling time (P< 0.05). PAA also had the largest reduction against Campylobacter population and prevalence during primary chilling (P< 0.01). Air chilling showed a lower reduction for Campylobacter than any immersion chilling intervention (P< 0.05). Chilling time and antimicrobial used during poultry processing had varying effects depending on the pathogen and outcome measure investigated (concentration or prevalence). High heterogeneity and low sample numbers in most analyses suggest that more high-quality research that is well-designed and has transparent reporting of methodology and results is needed to corroborate the results.

Despite the continuous progress in food science and technology, the global burden of foodborne illnesses remains substantial, with pathogens in food causing millions of infections each year. Traditional microbiological culture methods are inadequate in detecting the full spectrum of these microorganisms, highlighting the need for more comprehensive detection strategies. This review paper aims to elucidate the relationship between foodborne pathogen colonization and the composition of the poultry microbiome, and how this knowledge can be used for improved food safety. Our review highlights that the relationship between pathogen colonization varies across different sections of the poultry microbiome. Further, our review suggests that the microbiome profile of poultry litter, farm soil, and farm dust may serve as potential indicators of the farm environment's food safety issues. We also agree that the microbiome of processed chicken samples may reveal potential pathogen contamination and food quality issues. In addition, utilizing predictive modeling techniques on the collected microbiome data, we suggest establishing correlations between particular taxonomic groups and the colonization of pathogens, thus providing insights into food safety, and offering a comprehensive overview of the microbial community. In conclusion, this review underscores the potential of microbiome analysis as a powerful tool in food safety, pathogen detection, and risk assessment.

IntroductionThe occurrence of antibiotic resistant (ABR) bacteria in foods is a growing public health challenge. We evaluated sanitizer cross-tolerance among ABR Escherichia coli (E. coli) O157:H7 and non-O157:H7 Shiga-toxin producing E. coli (STEC) serogroups. Sanitizer tolerance in STEC could be a public health concern as mitigation strategies against the pathogen might be compromised.MethodsResistance to ampicillin and streptomycin were evolved in E. coli serogroups: O157:H7 (H1730, and ATCC 43895), O121:H19 and O26:H11. Resistance to antibiotics was evolved chromosomally through incremental exposure to ampicillin (amp C) and streptomycin (strep C). Transformation using a plasmid was performed to confer resistance to ampicillin to generate amp P strep C.ResultsThe minimum inhibitory concentration (MIC) of lactic acid for all strains evaluated was 0.375% v/v. Analysis of bacterial growth parameters in tryptic soy broth amended with 0.0625% v/v, 0.125% v/v, and 0.25% v/v (subMIC) lactic acid indicated that growth correlated positively with the lag phase duration, and negatively with both the maximum growth rate and change in population density for all strains evaluated except for the highly tolerant variant- O157:H7 amp P strep C. Strains O121 NR (non-ABR), O121 amp C, O121 amp P strep C, O157:H7 H1730 amp C and O157:H7 H1730 amp P strep C were not inactivated after exposure to 1% and 2.5% v/v lactic acid for 300 s. No recovery of cells was observed after the strains were exposed to 5% v/v lactic acid for 300 s. ABR strains O157:H7 H1730 amp C and O157: H7 H1730 amp P strep C demonstrated a high tolerance to lactic acid (P ≤ 0.05).ConclusionABR in isolate E. coli O157: H7 H1730 may improve tolerance to lactic acid. Increased tolerance may be discerned by evaluating growth parameters of bacteria in presence of sub-MIC levels of lactic acid.

Background: While long-term studies on the correlates of protection, vaccine effectiveness, and enhanced surveillance are awaited for SARS-CoV-2 vaccine, studies on breakthrough infections help understand the nature and course of this illness among vaccinated individuals and guide in public health preparedness. This study aims to compare the differences in the hospitalization outcomes SARS-CoV-2 infection of fully vaccinated individuals with with those of unvaccinated and partially vaccinated individuals. Materials and Methods: Single institution observational cohort study. This study compared the differences in clinical, biochemical parameters and the hospitalization outcomes of 53 fully vaccinated individuals with those of unvaccinated (1464) and partially vaccinated (231) individuals, among a cohort of 2,080 individuals hospitalized with SARS-CoV-2 infection. Descriptive statistics and propensity-score weighted multivariate logistic regression analysis adjusting for clinical and laboratory parameters were used to compare the differences and to identify factors associated with outcomes. Results: Completing the course of vaccination protected individuals from developing severe COVID-19 as evidenced by lower proportions of those with hypoxia, abnormal levels of inflammatory markers, requiring ventilatory support, and death compared to unvaccinated and partially vaccinated individuals. There were no differences in these outcomes among patients who received either vaccine type approved in India. Conclusions: Efforts should be made to improve the vaccination rates as a timely measure to prepare for the upcoming waves of this highly transmissible pandemic. Vaccination rates of the communities may also guide in the planning of the health needs and appropriate use of medical resources.

Background: Hypoxia in patients with COVID-19 is one of the strongest predictors of mortality. Silent hypoxia is characterised by the presence of hypoxia without dyspnoea. Silent hypoxia has been shown to affect the outcome in previous studies. Methods: This was a retrospective study of a cohort of patients with SARS-CoV-2 infection who were hypoxic at presentation. Clinical, laboratory and treatment parameters in patients with silent hypoxia and dyspnoeic hypoxia were compared. Multivariate logistic regression models were fitted to identify the factors predicting mortality. Results: Among 2080 patients with COVID-19 admitted to our hospital, 811 patients were hypoxic with SpO2

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: Recently, there has been a consumer push for natural and organic food products. This has caused alternative poultry production, such as organic, pasture, and free-range systems, to grow in popularity. Due to the stricter rearing practices of alternative poultry production systems, different types of levels of microbiological risks might be present for these systems when compared to conventional production systems. Both conventional and alternative production systems have complex supply chains that present many different opportunities for flocks of birds or poultry meat to be contaminated with foodborne pathogens. As such, it is important to understand the risks involved during each step of production. The purpose of this review is to detail the potential routes of foodborne pathogen transmission throughout the conventional and alternative supply chains, with a special emphasis on the differences in risk between the two management systems, and to identify gaps in knowledge that could assist, if addressed, in poultry risk-based decision making.

ABSTRACT: 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.

In December, 2019, a highly infectious and rapidly spreading new pneumonia of unknown cause was reported to the Chinese WHO Country Office. A cluster of these cases had appeared in Wuhan, a city in the Hubei Province of China. These infections were found to be caused by a new coronavirus which was given the name “2019 novel coronavirus” (2019-nCoV). It was later renamed “severe acute respiratory syndrome coronavirus 2,” or SARS-CoV-2 by the International Committee on Taxonomy of Viruses on February 11, 2020. It was named SARS-CoV-2 due to its close genetic similarity to the coronavirus which caused the SARS outbreak in 2002 (SARS-CoV-1). The aim of this review is to provide information, primarily to the food industry, regarding a range of biocides effective in eliminating or reducing the presence of coronaviruses from fomites, skin, oral/nasal mucosa, air, and food contact surfaces. As several EPA approved sanitizers against SARS-CoV-2 are commonly used by food processors, these compounds are primarily discussed as much of the industry already has them on site and is familiar with their application and use. Specifically, we focused on the effects of alcohols, povidone iodine, quaternary ammonium compounds, hydrogen peroxide, sodium hypochlorite (NaOCl), peroxyacetic acid (PAA), chlorine dioxide, ozone, ultraviolet light, metals, and plant-based antimicrobials. This review highlights the differences in the resistance or susceptibility of different strains of coronaviruses, or similar viruses, to these antimicrobial agents.

In the last decade, the proactive diagnosis of diseases with artificial intelligence and its aligned technologies has been an exciting and fruitful area. One of the areas in medical care where constant monitoring is required is cardiovascular diseases. Arrhythmia, one of the cardiovascular diseases, is generally diagnosed by doctors using Electrocardiography (ECG), which records the heart’s rhythm and electrical activity. The use of neural networks has been extensively adopted to identify abnormalities in the last few years. It is found that the probability of detecting arrhythmia increases if the denoised signal is used rather than the raw input signal. This paper compares six filters implemented on ECG signals to improve classification accuracy. Custom convolutional neural networks (CCNNs) are designed to filter ECG data. Extensive experiments are drawn by considering the six ECG filters and the proposed custom CCNN models. Comparative analysis reveals that the proposed models outperform the competitive models in various performance metrics.

Predictive models offer food scientists, farmers, and processors tools to help identify variables that lead to an increase in the food safety risk of a product. Foodborne pathogens, such as Listeria spp., pose a major problem for the pastured poultry industry. Currently, there is a lack of understanding of what farm practices lead to higher prevalence of Listeria spp. This study constructed random forest (RF) models to predict the prevalence of Listeria spp. in pastured poultry farming environments and the final broiler product based on major farm practices and variables. Feces, soil, and whole carcass rinse samples were collected from 11 farms in the southeastern United States and evaluated for Listeria spp. presence. The preharvest sample RF model identified the time of year and age of the broiler flock at time of sampling as factors of increased probability of Listeria spp. presence in feces and soil samples. The final product RF model identified brood feed and the presence of chlorine in processing rinse water as the two most important variables associated with an increased likelihood of Listeria spp. presence. Both the preharvest RF model and final sample RF model performed well on a held-out test set, with area under the receiver operating characteristic curve values of 0.876 and 0.887, respectively. The presented models showed the usefulness of RF models in a food safety context. Both RF models will help pastured poultry farmers and processors guide control strategies to manage Listeria contamination in pastured poultry farms and products.