Literature DB >> 25156259

Quantitative risk assessment of haemolytic and uremic syndrome linked to O157:H7 and non-O157:H7 Shiga-toxin producing Escherichia coli strains in raw milk soft cheeses.

Frédérique Perrin1, Fanny Tenenhaus-Aziza, Valérie Michel, Stéphane Miszczycha, Nadège Bel, Moez Sanaa.   

Abstract

Shiga-toxin producing Escherichia coli (STEC) strains may cause human infections ranging from simple diarrhea to Haemolytic Uremic Syndrome (HUS). The five main pathogenic serotypes of STEC (MPS-STEC) identified thus far in Europe are O157:H7, O26:H11, O103:H2, O111:H8, and O145:H28. Because STEC strains can survive or grow during cheese making, particularly in soft cheeses, a stochastic quantitative microbial risk assessment model was developed to assess the risk of HUS associated with the five MPS-STEC in raw milk soft cheeses. A baseline scenario represents a theoretical worst-case scenario where no intervention was considered throughout the farm-to-fork continuum. The risk level assessed with this baseline scenario is the risk-based level. The impact of seven preharvest scenarios (vaccines, probiotic, milk farm sorting) on the risk-based level was expressed in terms of risk reduction. Impact of the preharvest intervention ranges from 76% to 98% of risk reduction with highest values predicted with scenarios combining a decrease of the number of cow shedding STEC and of the STEC concentration in feces. The impact of postharvest interventions on the risk-based level was also tested by applying five microbiological criteria (MC) at the end of ripening. The five MCs differ in terms of sample size, the number of samples that may yield a value larger than the microbiological limit, and the analysis methods. The risk reduction predicted varies from 25% to 96% by applying MCs without preharvest interventions and from 1% to 96% with combination of pre- and postharvest interventions.
© 2014 Society for Risk Analysis.

Entities:  

Keywords:  Microbiological criteria; STEC; preharvest intervention; quantitative risk assessment model; raw milk soft cheese

Mesh:

Year:  2014        PMID: 25156259     DOI: 10.1111/risa.12267

Source DB:  PubMed          Journal:  Risk Anal        ISSN: 0272-4332            Impact factor:   4.000


  6 in total

Review 1.  Quantitative Microbial Risk Assessment and Infectious Disease Transmission Modeling of Waterborne Enteric Pathogens.

Authors:  Andrew F Brouwer; Nina B Masters; Joseph N S Eisenberg
Journal:  Curr Environ Health Rep       Date:  2018-06

2.  Microbial Risk Assessment of Non-Enterohemorrhagic Escherichia coli in Natural and Processed Cheeses in Korea.

Authors:  Kyungmi Kim; Heeyoung Lee; Soomin Lee; Sejeong Kim; Jeeyeon Lee; Jimyeong Ha; Yohan Yoon
Journal:  Korean J Food Sci Anim Resour       Date:  2017-08-31       Impact factor: 2.622

3.  Cheese Microbial Risk Assessments - A Review.

Authors:  Kyoung-Hee Choi; Heeyoung Lee; Soomin Lee; Sejeong Kim; Yohan Yoon
Journal:  Asian-Australas J Anim Sci       Date:  2016-03-01       Impact factor: 2.509

4.  Milk Fat Globules Hamper Adhesion of Enterohemorrhagic Escherichia coli to Enterocytes: In Vitro and in Vivo Evidence.

Authors:  Thomas Douëllou; Wessam Galia; Stéphane Kerangart; Thierry Marchal; Nadège Milhau; Renaud Bastien; Marion Bouvier; Samuel Buff; Marie-Christine Montel; Delphine Sergentet-Thevenot
Journal:  Front Microbiol       Date:  2018-05-15       Impact factor: 5.640

5.  Bacterial-fungal interactions revealed by genome-wide analysis of bacterial mutant fitness.

Authors:  Emily C Pierce; Manon Morin; Jessica C Little; Roland B Liu; Joanna Tannous; Nancy P Keller; Kit Pogliano; Benjamin E Wolfe; Laura M Sanchez; Rachel J Dutton
Journal:  Nat Microbiol       Date:  2020-11-02       Impact factor: 17.745

Review 6.  Shiga Toxin-Producing Escherichia coli and Milk Fat Globules.

Authors:  Arthur Bagel; Delphine Sergentet
Journal:  Microorganisms       Date:  2022-02-23
  6 in total

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