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The transfer ratio of bacteria from one surface to another is often estimated from laboratory experiments and quantified by dividing the expected number of bacteria on the recipient surface by the expected number of bacteria on the donor surface. Yet, the expected number of bacteria on each surface is uncertain due to the limited number of colonies that are counted and/or samples that can be analyzed. The expected transfer ratio is, therefore, also uncertain and its estimate may exceed 1 if real transfer is close to 100%. In addition, the transferred fractions vary over experiments but it is unclear, using this approach, how to combine uncertainty and variability into one estimate for the transfer ratio. A Bayesian network model was proposed that allows the combination of uncertainty within one experiment and variability over multiple experiments and prevents inappropriate values for the transfer ratio. Model functionality was shown using data from a laboratory experiment in which the transfer of Salmonella was determined from contaminated pork meat to a butcher's knife, and vice versa. Recovery efficiency of bacteria from both surfaces was also determined and accounted for in the analysis. Transfer ratio probability distributions showed a large variability, with a mean value of 0.19 for the transfer of Salmonella from pork meat to the knife and 0.58 for the transfer of Salmonella from the knife to pork meat. The proposed Bayesian model can be used for analyzing data from similar study designs in which uncertainty should be combined with variability.

International audience ; According to the World Health Organization estimates in 2015, 600 million people fall ill every year from contaminated food and 420,000 die. Microbial risk assessment (MRA) was developed as a tool to reduce and prevent risks presented by pathogens and/or their toxins. MRA is organized in four steps to analyse information and assist in both designing appropriate control options and implementation of regulatory decisions and programs. Among the four steps, hazard characterisation is performed to establish the probability and severity of a disease outcome, which is determined as function of the dose of toxin and/or pathogen ingested. This dose-response relationship is subject to both variability and uncertainty. The purpose of this review/opinion article is to discuss how Next Generation Omics can impact hazard characterisation and, more precisely, how it can improve our understanding of variability and limit the uncertainty in the dose-response relation. The expansion of omics tools (e.g. genomics, transcriptomics, proteomics and metabolomics) allows for a better understanding of pathogenicity mechanisms and virulence levels of bacterial strains. Detection and identification of virulence genes, comparative genomics, analyses of mRNA and protein levels and the development of biomarkers can help in building a mechanistic dose-response model to predict disease severity. In this respect, systems biology can help to identify critical system characteristics that confer virulence and explain variability between strains. Despite challenges in the integration of omics into risk assessment, some omics methods have already been used by regulatory agencies for hazard identification. Standardized methods, reproducibility and datasets obtained from realistic conditions remain a challenge, and are needed to improve accuracy of huard characterisation. When these improvements are realized, they will allow the health authorities and government policy makers to prioritize hazards more accurately and thus ...

According to the World Health Organization estimates in 2015, 600 million people fall ill every year from contaminated food and 420,000 die. Microbial risk assessment (MRA) was developed as a tool to reduce and prevent risks presented by pathogens and/or their toxins. MRA is organized in four steps to analyse information and assist in both designing appropriate control options and implementation of regulatory decisions and programs. Among the four steps, hazard characterisation is performed to establish the probability and severity of a disease outcome, which is determined as function of the dose of toxin and/or pathogen ingested. This dose-response relationship is subject to both variability and uncertainty. The purpose of this review/opinion article is to discuss how Next Generation Omics can impact hazard characterisation and, more precisely, how it can improve our understanding of variability and limit the uncertainty in the dose-response relation. The expansion of omics tools (e.g. genomics, transcriptomics, proteomics and metabolomics) allows for a better understanding of pathogenicity mechanisms and virulence levels of bacterial strains. Detection and identification of virulence genes, comparative genomics, analyses of mRNA and protein levels and the development of biomarkers can help in building a mechanistic dose-response model to predict disease severity. In this respect, systems biology can help to identify critical system characteristics that confer virulence and explain variability between strains. Despite challenges in the integration of omics into risk assessment, some omics methods have already been used by regulatory agencies for hazard identification. Standardized methods, reproducibility and datasets obtained from realistic conditions remain a challenge, and are needed to improve accuracy of huard characterisation. When these improvements are realized, they will allow the health authorities and government policy makers to prioritize hazards more accurately and thus refine surveillance programs with the collaboration of all stakeholders of the food chain.