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Dive into the research topics where Caroline Bidot is active.

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Featured researches published by Caroline Bidot.


PLOS ONE | 2014

Integrative Model of the Immune Response to a Pulmonary Macrophage Infection: What Determines the Infection Duration?

Natacha Go; Caroline Bidot; Catherine Belloc; Suzanne Touzeau

The immune mechanisms which determine the infection duration induced by pathogens targeting pulmonary macrophages are poorly known. To explore the impact of such pathogens, it is indispensable to integrate the various immune mechanisms and to take into account the variability in pathogen virulence and host susceptibility. In this context, mathematical models complement experimentation and are powerful tools to represent and explore the complex mechanisms involved in the infection and immune dynamics. We developed an original mathematical model in which we detailed the interactions between the macrophages and the pathogen, the orientation of the adaptive response and the cytokine regulations. We applied our model to the Porcine Respiratory and Reproductive Syndrome virus (PRRSv), a major concern for the swine industry. We extracted value ranges for the model parameters from modelling and experimental studies on respiratory pathogens. We identified the most influential parameters through a sensitivity analysis. We defined a parameter set, the reference scenario, resulting in a realistic and representative immune response to PRRSv infection. We then defined scenarios corresponding to graduated levels of strain virulence and host susceptibility around the reference scenario. We observed that high levels of antiviral cytokines and a dominant cellular response were associated with either short, the usual assumption, or long infection durations, depending on the immune mechanisms involved. To identify these mechanisms, we need to combine the levels of antiviral cytokines, including , and . The latter is a good indicator of the infected macrophage level, both combined provide the adaptive response orientation. Available PRRSv vaccines lack efficiency. By integrating the main interactions between the complex immune mechanisms, this modelling framework could be used to help designing more efficient vaccination strategies.


Journal of Nutrition | 2011

Fat-Free Mass Predictions through a Bayesian Network Enable Body Composition Comparisons in Various Populations

Laurence Mioche; Alain Brigand; Caroline Bidot; Jean-Baptiste Denis

The respective contribution of fat-free mass (FFM) and fat mass to body weight (Wgt) is a relevant indicator of risk for major public health issues. In an earlier study, a Bayesian Network (BN) was designed to predict FFM from a DXA database (1999-2004 NHANES, n = 10,402) with easily accessible variables [sex, age, Wgt, and height (Hgt)]. The objective of the present study was to assess the robustness of these BN predictions in different population contexts (age, BMI, ethnicity, etc.) when covariables were stochastically deduced from population-based distributions. BN covariables were adjusted to 82 published distributions for age, Wgt, and Hgt from 16 studies assessing body composition. Anthropometric adjustments required a surrogate database (n = 23,411) to get the missing correlation between published Wgt and Hgt distributions. Published BMI distributions and their predicted BN counterparts were correlated (R(2) = 0.99; P < 0.001). Predicted FFM distributions were closely adjusted to their published counterparts for both sexes between 20 and 79 y old, with some discrepancies for Asian populations. In addition, BN predictions revealed a very good agreement between FFM assessed in different population contexts. The mean difference between published FFM values (61.1 ± 3.44 and 42.7 ± 3.32 kg for men and women, respectively) and BN predictions (61.6 ± 3.11 and 42.4 ± 2.76 kg for men and women, respectively) was <1% when FFM was assessed by DXA; the difference rose to 3.6% when FFM was assessed by bioelectric impedance analysis or by densitometry methods. These results suggest that it is possible, within certain anthropometric limitations, to use BN predictions as a complementary body composition analysis for large populations.


Archive | 2010

A branching process approach for the propagation of the Bovine Spongiform Encephalopathy in Great-Britain

Christine Jacob; Laurence Maillard-Teyssier; Jean-Baptiste Denis; Caroline Bidot

The goal of this work is the modelling of the propagation of BSE (Bovine Spongiform Encephalopathy) at the scale of a very large population (Great-Britain) in order to predict its extinction time and to evaluate the efficiency of the main feedban regulation. To this end, we first elaborated a multitype branching process in discrete time with age and population dependent individual transitions. The types are the health states at each age. Then, assuming that the disease is rare at the initial time, and assuming that the probability for an animal to be exposed to a given infective is inversely proportional to the total population size, we derived from this model, as the initial size of the population increases to ∞, a limit process on the incidence of clinical cases. This limit process may be either considered as a singletype d-Markovian process with a Poissonian transition distribution, or a multitype Bienayme–Galton–Watson process having d types corresponding to the memory of the process. We studied the behavior of the limit process and estimated its unknown parameters using a Bayesian approach.


British Journal of Nutrition | 2011

Body composition predicted with a Bayesian network from simple variables.

Laurence Mioche; Caroline Bidot; Jean-Baptiste Denis


6th. International Conference predictive Modeling in Foods | 2009

Identification of complex microbiological dynamic systems by nonlinear filtering

Jean-Pierre Gauchi; Caroline Bidot; J.C. Augustin; Jean-Pierre Vila


41èmes Journées de Statistique, SFdS, Bordeaux | 2009

Identification de systèmes dynamiques microbiologiques complexes par filtrage non linéaire

Jean-Pierre Vila; Jean-Pierre Gauchi; Caroline Bidot


Archive | 2016

Modélisation du portage des salmonelles dans un élevage porcin

Justine Guillaumont; Caroline Bidot; Suzanne Touzeau


Emerging Trends in Applied Mathematics and Mechanics (ETAMM) | 2016

Why, when and how should exposure be considered in a model representing the within-host immune response to infection?

Suzanne Touzeau; Natacha Go; Caroline Bidot; Catherine Belloc


Archive | 2015

Comparisons in Various Populations Fat-Free Mass Predictions through a Bayesian Network Enable Body Composition

M. O. Thorner; M. L. Hartman; Arthur Weltman; J. A. Kanaley; L. Wideman; Steven B. Heymsfield; C. D. Teates; Je Williams; Jonathan C. K. Wells; Catherine M Wilson; Dalia Haroun; A Lucas; Mary S; Laurence Mioche; Alain Brigand; Caroline Bidot; Jean-Baptiste Denis


International Porcine Reproductive And Respiratory Syndrome Congress | 2015

Towards a better understanding of the within-host dynamics to PRRSv: insights from modelling approach

Natacha Go; Catherine Belloc; Caroline Bidot; Suzanne Touzeau

Collaboration


Dive into the Caroline Bidot's collaboration.

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Natacha Go

Institut national de la recherche agronomique

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Suzanne Touzeau

Institut national de la recherche agronomique

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Jean-Baptiste Denis

Institut national de la recherche agronomique

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Jean-Pierre Gauchi

Institut national de la recherche agronomique

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Jean-Pierre Vila

Institut national de la recherche agronomique

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Laurence Mioche

Institut national de la recherche agronomique

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Steven B. Heymsfield

Pennington Biomedical Research Center

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A Lucas

University College London

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