Biomedical subjects

K Bernaerts

Publications and source records attributed to K Bernaerts.

6 recordsLinked to original sources

PubMed2004

Predictive modelling of the microbial lag phase: a review.

This paper summarises recent trends in predictive modelling of microbial lag phenomena. The lag phase is approached from both a qualitative and a quantitative point of view. First, a definition of lag and an analysis of the prevailing measuring techniques for the determination of lag time is presented. Furthermore, based on experimental results presented in literature, factors influencing the lag phase are discussed. Major modelling approaches concerning lag phase estimation are critically assessed. In predictive microbiology, a two-step modelling approach is used. Primary models describe the evolution of microbial numbers with time and can be subdivided into deterministic and stochastic models. Primary deterministic models, e.g., Baranyi and Roberts [Int. J. Food Microbiol. 23 (1994) 277], Hills and Wright [J. Theor. Biol. 168 (1994) 31] and McKellar [Int. J. Food Microbiol. 36 (1997) 179], describe the evolution of microorganisms, using one single (deterministic) set of model parameters. In stochastic models, e.g., Buchanan et al. [Food Microbiol. 14 (1997) 313], Baranyi [J. Theor. Biol. 192 (1998) 403] and McKellar [J. Appl. Microbiol. 90 (2001) 407], the model parameters are distributed or random variables. Secondary models describe the relation between primary model parameters and influencing factors (e.g., environmental conditions). This survey mainly focuses on the influence of temperature and culture history on the lag phase during growth of bacteria.

Bacteria↗

PubMed2001

Quantitative analysis of bacterial gene expression by using the gusA reporter gene system.

An Azospirillum brasilense Sp7 strain containing a plasmid-borne translational cytN-gusA fusion was grown in a continuous culture to quantitatively evaluate the influence of extracellular signals (such as O(2)) on expression of the cytNOQP operon. The dissolved oxygen concentration was shifted at regular time intervals before the steady state was reached. The measured beta-glucuronidase activity was used to monitor cytN gene expression. However, as the beta-glucuronidase activity in the experimental setup not only depended on altered transcription of the hybrid gene when the signal was varied but was also influenced by cellular accumulation, degradation, and dilution of the hybrid fusion protein, a mathematical method was developed to describe the intrinsic properties of the dynamic bioprocess. After identification and validation of the mathematical model, the apparent specific rate of expression of the fusion, which was independent of the experimental setup, could be deduced from the model and used to quantify gene expression regulated by extracellular environmental signals. In principle, this approach can be generalized to assess the effects of external signals on bacterial gene expression.

Azospirillum brasilense↗

PubMed2000

On the design of optimal dynamic experiments for parameter estimation of a Ratkowsky-type growth kinetics at suboptimal temperatures.

It is generally known that accurate model building, i.e., proper model structure selection and reliable parameter estimation, constitutes an essential matter in the field of predictive microbiology, in particular, when integrating these predictive models in food safety systems. In this context, Versyck et al. (1999) have introduced the methodology of optimal experimental design techniques for parameter estimation within the field. Optimal experimental design focuses on the development of optimal input profiles such that the resulting rich (i.e., highly informative) experimental data enable unique model parameter estimation. As a case study, Versyck et al. (1999) [Versyck, K., Bernaerts, K., Geeraerd, A.H., Van Impe, J.F., 1999. Introducing optimal experimental design in predictive modeling: a motivating example. Int. J. Food Microbiol., 51(1), 39-51] have elaborated the estimation of Bigelow inactivation kinetics parameters (in a numerical way). Opposed to the classic (static) experimental approach in predictive modelling, an optimal dynamic experimental setup is presented. In this paper, the methodology of optimal experimental design or parameter estimation is applied to obtain uncorrelated estimates of the square root model parameters [Ratkowsky, D.A., Olley, J., McMeekin, T.A., Ball, A., 1982. Relationship between temperature and growth rate of bacterial cultures. J. Bacteriol. 149, 1-5] describing the effect of suboptimal growth temperatures on the maximum specific growth rate of microorganisms. These estimates are the direct result of fitting a primary growth model to cell density measurements as a function of time. Apart from the design of an optimal time-varying temperature profile based on a sensitivity study of the model output, an important contribution of this publication is a first experimental validation of this innovative dynamic experimental approach for uncorrelated parameter identification. An optimal step temperature profile, within the range of model validity and practical feasibility, is developed for Escherichia coli K12 and successfully applied in practice. The presented experimental validation result illustrates the large potential of the dynamic experimental approach in the context of uncorrelated parameter estimation. Based on the experimental validation result, additional remarks are formulated related to future research in the field of optimal experimental design.

Escherichia coli↗

PubMed2000

Frequency of intravenous medication administration to hospitalised patients: secondary data-analysis of the Belgian nursing minimum data set.

The purpose of this study was to investigate the frequency of intravenous medication administration with Belgian hospitalised patients. Factors, which might influence this frequency of administration, were also studied. Research questions were investigated by secondary data-analysis of the Belgian Nursing Minimum Data Set. The randomised sample consisted of 1,035,681 observations on 421,530 patients. Results of this study demonstrate that one out of three (34%) hospitalised patients received intravenous medication. Medical diagnoses, for which most intravenous medications were administered, were oncological diseases: myeloid (77.9%) and lymphoid (69.4%) leukaemia. Elderly (6.7%) and female (31.2%) patients received significantly less intravenous medication than respectively young (32.9%) (chi(2) = 98411, df = 1, p<0.001) and male (38%) (chi(2) = 2033, df = 1, p<0.001) patients. Patients with intravenous medication administration were labour intensive for nursing staff.

Adolescent↗

PubMed1999

Introducing optimal experimental design in predictive modeling: a motivating example.

Predictive microbiology emerges more and more as a rational quantitative framework for predicting and understanding microbial evolution in food products. During the mathematical modeling of microbial growth and/or inactivation, great, but not always efficient, effort is spent on the determination of the model parameters from experimental data. In order to optimize experimental conditions with respect to parameter estimation, experimental design has been extensively studied since the 1980s in the field of bioreactor engineering. The so-called methodology of optimal experimental design established in this research area enabled the reliable estimation of model parameters from data collected in well-designed fed-batch reactor experiments. In this paper, we introduce the optimal experimental design methodology for parameter estimation in the field of predictive microbiology. This study points out that optimal design of dynamic input signals is necessary to maximize the information content contained within the resulting experimental data. It is shown that from few dynamic experiments, more pertinent information can be extracted than from the classical static experiments. By introducing optimal experimental design into the field of predictive microbiology, a new promising frame for maximization of the information content of experimental data with respect to parameter estimation is provided. As a case study, the design of an optimal temperature profile for estimation of the parameters D(ref) and z of an Arrhenius-type model for the maximum inactivation rate kmax as a function of the temperature, T, was considered. Microbial inactivation by heating is described using the model of Geeraerd et al. (1999). The need for dynamic temperature profiles in experiments aimed at the simultaneous estimation of the model parameters from measurements of the microbial population density is clearly illustrated by analytical elaboration of the mathematical expressions involved on the one hand, and by numerical simulations on the other.

Bacteria↗

PubMed1998

Protective effect of calcium on inactivation of Escherichia coli by high hydrostatic pressure.

The effect of divalent cations on the inactivation of Escherichia coli by high hydrostatic pressure was investigated. The presence of 0.5 mmol l-1 of CaCl2, MgCl2, MnCl2 and FeCl2 reduced pressure inactivation of E. coli MG1655, while 0.5 mmol l-1 of ZnCl2, NiCl2, CuCl2 and CoCl2 increased inactivation. Baroprotection by Ca2+ was found to be dose-dependent up to at least 80 mmol l-1 and was studied in more detail in terms of inactivation kinetics. Logarithmic survivor plots against time deviated from first order kinetics, suggesting that MG1655 cultures were heterogeneous with regard to pressure resistance. All cultures were shown to contain a small proportion of cells that were only slowly inactivated. Addition of Ca2+ increased the proportion of these tolerant cells in the cultures up to 1000-fold at 80 mmol l-1, but did not affect their inactivation rate. The addition of EDTA resulted in the opposite effect, lowering the proportion of pressure-tolerant cells in the cultures. Three pressure-resistant mutants of E. coli MG1655 were found to be more resistant to EDTA under pressure compared with MG1655, and were unaffected by Ca2+ under pressure. In addition, these mutants had a 30-40% lower Ca2+ content than MG1655. Based on these results, it is postulated that pressure killing of E. coli MG1655 is mediated primarily by the destabilization of Ca(2+)-binding components, and that the mutations underlying pressure resistance have resulted in pressure-stable targets with reduced Ca(2+)-binding affinity.

Calcium↗