BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

163 related articles for article (PubMed ID: 29216553)

  • 1. Mathematical quantification of the induced stress resistance of microbial populations during non-isothermal stresses.
    Garre A; Huertas JP; González-Tejedor GA; Fernández PS; Egea JA; Palop A; Esnoz A
    Int J Food Microbiol; 2018 Feb; 266():133-141. PubMed ID: 29216553
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Mathematical modelling of the stress resistance induced in Listeria monocytogenes during dynamic, mild heat treatments.
    Garre A; González-Tejedor GA; Aznar A; Fernández PS; Egea JA
    Food Microbiol; 2019 Dec; 84():103238. PubMed ID: 31421752
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Evaluation of the Gauss-Eyring model to predict thermal inactivation of micro-organisms at short holding times.
    Timmermans RAH; Mastwijk HC; Nierop Groot MN; Van Boekel MAJS
    Int J Food Microbiol; 2017 Dec; 263():47-60. PubMed ID: 29031104
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Relevance of the Induced Stress Resistance When Identifying the Critical Microorganism for Microbial Risk Assessment.
    Garre A; Egea JA; Iguaz A; Palop A; Fernandez PS
    Front Microbiol; 2018; 9():1663. PubMed ID: 30087669
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Stress-adaptive responses by heat under the microscope of predictive microbiology.
    Valdramidis VP; Geeraerd AH; Van Impe JF
    J Appl Microbiol; 2007 Nov; 103(5):1922-30. PubMed ID: 17953602
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Estimation of Listeria monocytogenes survival during thermoultrasonic treatments in non-isothermal conditions: Effect of ultrasound on temperature and survival profiles.
    Franco-Vega A; Ramírez-Corona N; López-Malo A; Palou E
    Food Microbiol; 2015 Dec; 52():124-30. PubMed ID: 26338125
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A quasi-chemical model for the growth and death of microorganisms in foods by non-thermal and high-pressure processing.
    Doona CJ; Feeherry FE; Ross EW
    Int J Food Microbiol; 2005 Apr; 100(1-3):21-32. PubMed ID: 15854689
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Different model hypotheses are needed to account for qualitative variability in the response of two strains of Salmonella spp. under dynamic conditions.
    Georgalis L; Psaroulaki A; Aznar A; Fernández PS; Garre A
    Food Res Int; 2022 Aug; 158():111477. PubMed ID: 35840198
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Prediction of an organism's inactivation patterns from three single survival ratios determined at the end of three non-isothermal heat treatments.
    Corradini MG; Normand MD; Peleg M
    Int J Food Microbiol; 2008 Aug; 126(1-2):98-111. PubMed ID: 18579249
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Bioinactivation FE: A free web application for modelling isothermal and dynamic microbial inactivation.
    Garre A; Clemente-Carazo M; Fernández PS; Lindqvist R; Egea JA
    Food Res Int; 2018 Oct; 112():353-360. PubMed ID: 30131146
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Effect of heat activation and inactivation conditions on germination and thermal resistance parameters of Bacillus cereus spores.
    Fernández A; Ocio MJ; Fernández PS; Martínez A
    Int J Food Microbiol; 2001 Feb; 63(3):257-64. PubMed ID: 11246909
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Quantifying the effects of heating temperature, and combined effects of heating medium pH and recovery medium pH on the heat resistance of Salmonella typhimurium.
    Leguérinel I; Spegagne I; Couvert O; Coroller L; Mafart P
    Int J Food Microbiol; 2007 May; 116(1):88-95. PubMed ID: 17292502
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Predicting heat process efficiency in thermal processes when bacterial inactivation is not log-linear.
    Desriac N; Vergos M; Achberger V; Coroller L; Couvert O
    Int J Food Microbiol; 2019 Feb; 290():36-41. PubMed ID: 30292677
    [TBL] [Abstract][Full Text] [Related]  

  • 14. On the use of the Weibull model to describe thermal inactivation of microbial vegetative cells.
    van Boekel MA
    Int J Food Microbiol; 2002 Mar; 74(1-2):139-59. PubMed ID: 11930951
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Modeling non-isothermal heat inactivation of microorganisms having biphasic isothermal survival curves.
    Corradini MG; Normand MD; Peleg M
    Int J Food Microbiol; 2007 May; 116(3):391-9. PubMed ID: 17395330
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Calculation of the non-isothermal inactivation patterns of microbes having sigmoidal isothermal semi-logarithmic survival curves.
    Peleg M
    Crit Rev Food Sci Nutr; 2003; 43(6):645-58. PubMed ID: 14669882
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Stochastic modeling of variability in survival behavior of Bacillus simplex spore population during isothermal inactivation at the single cell level using a Monte Carlo simulation.
    Abe H; Koyama K; Kawamura S; Koseki S
    Food Microbiol; 2019 Sep; 82():436-444. PubMed ID: 31027803
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Development of a log-quadratic model to describe microbial inactivation, illustrated by thermal inactivation of Clostridium botulinum.
    Stone G; Chapman B; Lovell D
    Appl Environ Microbiol; 2009 Nov; 75(22):6998-7005. PubMed ID: 19767461
    [TBL] [Abstract][Full Text] [Related]  

  • 19. An optimization algorithm for estimation of microbial survival parameters during thermal processing.
    Chen G; Campanella OH
    Int J Food Microbiol; 2012 Mar; 154(1-2):52-8. PubMed ID: 22244193
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Optimal experimental design for improving the estimation of growth parameters of Lactobacillus viridescens from data under non-isothermal conditions.
    Longhi DA; Martins WF; da Silva NB; Carciofi BA; de Aragão GM; Laurindo JB
    Int J Food Microbiol; 2017 Jan; 240():57-62. PubMed ID: 27427489
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.