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Quantification of Salmonella Survival and Infection in an In vitro Model of the Human Intestinal Tract as Proxy for Foodborne Pathogens.
Wijnands, Lucas M; Teunis, Peter F M; Kuijpers, Angelina F A; Delfgou-Van Asch, Ellen H M; Pielaat, Annemarie.
Afiliación
  • Wijnands LM; National Institute of Public Health and the EnvironmentBilthoven, Netherlands.
  • Teunis PFM; National Institute of Public Health and the EnvironmentBilthoven, Netherlands.
  • Kuijpers AFA; Rollins School of Public Health, Emory UniversityAtlanta, GA, United States.
  • Delfgou-Van Asch EHM; National Institute of Public Health and the EnvironmentBilthoven, Netherlands.
  • Pielaat A; National Institute of Public Health and the EnvironmentBilthoven, Netherlands.
Front Microbiol ; 8: 1139, 2017.
Article en En | MEDLINE | ID: mdl-28713334
ABSTRACT
Different techniques are available for assessing differences in virulence of bacterial foodborne pathogens. The use of animal models or human volunteers is not expedient for various reasons; the use of epidemiological data is often hampered by lack of crucial data. In this paper, we describe a static, sequential gastrointestinal tract (GIT) model system in which foodborne pathogens are exposed to simulated gastric and intestinal contents of the human digestive tract, including the interaction of pathogens with the intestinal epithelium. The system can be employed with any foodborne bacterial pathogens. Five strains of Salmonella Heidelberg and one strain of Salmonella Typhimurium were used to assess the robustness of the system. Four S. Heidelberg strains originated from an outbreak, the fifth S. Heidelberg strain and the S. Typhimurium strain originated from routine meat inspections. Data from plate counts, collected for determining the numbers of surviving bacteria in each stage, were used to quantify both the experimental uncertainty and biological variability of pathogen survival throughout the system. For this, a hierarchical Bayesian framework using Markov chain Monte Carlo (MCMC) was employed. The model system is able to distinguish serovars/strains for in vitro infectivity when accounting for within strain biological variability and experimental uncertainty.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Microbiol Año: 2017 Tipo del documento: Article País de afiliación: Países Bajos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Microbiol Año: 2017 Tipo del documento: Article País de afiliación: Países Bajos