Microbial Risk Analysis最新文献

RNA viruses exhibit a great tendency to mutate. Mutations occur in the parts of the genome that encode the spike glycoprotein and less often in the rest of the genome. This is why Gibbs energy of binding changes more than that of biosynthesis. Starting from 2019, the wild type that was labeled Hu-1 has during the last 3 years evolved to produce several dozen new variants, as a consequence of mutations. Mutations cause changes in empirical formulas of new virus strains, which lead to change in thermodynamic properties of biosynthesis and binding. These changes cause changes in the rate of reactions of binding of virus antigen to the host cell receptor and the rate of virus multiplication in the host cell. Changes in thermodynamic and kinetic parameters lead to changes in biological parameters of infectivity and pathogenicity. Since the beginning of the COVID-19 pandemic, SARS-CoV-2 has been evolving towards increase in infectivity and maintaining constant pathogenicity, or for some variants a slight decrease in pathogenicity. In the case of Omicron BQ.1, BQ.1.1, XBB and XBB.1 variants pathogenicity is identical as in the Omicron BA.2.75 variant. On the other hand, infectivity of the Omicron BQ.1, BQ.1.1, XBB and XBB.1 variants is greater than those of previous variants. This will most likely result in the phenomenon of asymmetric coinfection, that is circulation of several variants in the population, some being dominant.

Risk classification and management of shigatoxin-producing E. coli (STEC) isolated from food has been hampered by gaps in knowledge about the properties that determine the extent to which different subtypes of STEC can cause severe disease. Data on the proportion of infected human cases being affected by severe illness enables an evaluation of existing approaches for classifying STEC strains and the development of a new public health based approach. Evaluations show that existing approaches do not unequivocally classify different STEC variants according to their ability to cause severe disease. A new approach for ranking of STEC genotypes, combining the estimated probability of the strain to cause severe illness with the public health burden associated with the illness in terms of DALY per case, address these limitations. The result is a list of STEC genotypes in descending order of potential public health burden per case. The approach is risk based in considering the probability and consequences following infection (severe illness), and can support transparent risk management. This is illustrated by, arbitrarily, separating the ranked list of genotypes into classes based on the potential public health burden, and by characterising collections of strains isolated from different foods into different classes. Further, the classification of food samples as satisfactory or not based on the cost in terms of proportion of food being rejected and the benefit in terms of the proportion of strains causing severe illness (HUS) that are being captured is demonstrated using this approach.

Norovirus is a significant hazard to consumers of shellfish, in particular oysters. Oysters grown in waters contaminated with wastewater filter and accumulate norovirus particles, causing infection in humans when the product is consumed raw or lightly cooked. In the European Union (EU) and the United States, bacterial detection criteria are used to assess and manage microbial risk in shellfish. This regulatory framework is effective in managing the bacterial risks associated with microbiological contamination of bivalve shellfish but not for viruses. Although a standard detection method for norovirus in oyster exists (ISO 15,216–1:2017), no quantitative microbial risk assessment has been published that links the concentration of norovirus in oysters from a classified production area with consumer exposure. This study shows the successful development of a two-dimensional Monte Carlo exposure assessment model, taking an ISO 15,216–1:2017 detection result and producing an estimate of the resulting per-serving consumer exposure distribution. In contrast to previous oyster virus risk assessments, consumption is modelled using individual oysters as the unit, rather than total flesh weight. The variation in copies per oyster is modelled using a Poisson-lognormal distribution. The results show the boundaries for potential exposure following a given ISO detection result, and the relative importance of mean concentration, serving size, and oyster grade. This is directly relevant to potential regulatory thresholds being considered in the EU.

Objectives

Variants of a coronavirus (SARS-CoV-2) have been spreading in a global pandemic. Improved understanding of the infectivity of future new variants is important so that effective countermeasures against them can be quickly undertaken. In our research reported here, we aimed to predict the infectivity of SARS-CoV-2 by using a mathematical model with molecular simulation analysis, and we used phylogenetic analysis to determine the evolutionary distance of the spike protein gene (S gene) of SARS-CoV-2.

Methods

We subjected the six variants and the wild type of spike protein and human angiotensin-converting enzyme 2 (ACE2) to molecular docking simulation analyses to understand the binding affinity of spike protein and ACE2. We then utilized regression analysis of the correlation coefficient of the mathematical model and the infectivity of SARS-CoV-2 to predict infectivity.

Results

The evolutionary distance of the S gene correlated with the infectivity of SARS-CoV-2 variants. The calculated biding affinity for the mathematical model obtained with results of molecular docking simulation also correlated with the infectivity of SARS-CoV-2 variants. These results suggest that the data from the docking simulation for the receptor binding domain of variant spike proteins and human ACE2 were valuable for prediction of SARS-CoV-2 infectivity.

Conclusion

We developed a mathematical model for prediction of SARS-CoV-2 variant infectivity by using binding affinity obtained via molecular docking and the evolutionary distance of the S gene.

Ebola virus is among the most dangerous, contagious and deadly etiological causes of viral diseases. However, Ebola virus has never extensively spread in human population and never have led to a pandemic. Why? The mechanistic biophysical model revealing the biothermodynamic background of virus-host interaction) could help us to understand pathogenesis of Ebola virus disease (earlier known as the Ebola hemorrhagic fever). In this paper for the first time the empirical formula, thermodynamic properties of biosynthesis (including the driving force of virus multiplication in the susceptible host), binding constant and thermodynamic properties of binding are reported. Thermodynamic data for Ebola virus were compared with data for SARS-CoV-2 to explain why SARS-CoV-2 has caused a pandemic, while Ebola remains on local epidemic level. The empirical formula of the Ebola virus was found to be CH_1.569O_0.3281N_0.2786P_0.00173S_0.00258. Standard Gibbs energy of biosynthesis of the Ebola virus nucleocapsid is -151.59 kJ/C-mol.

From march 2020 to march 2022 covid-19 has shown a consistent pattern of increasing infections during the Winter and low infection numbers during the Summer. Understanding the effects of seasonal variation on covid-19 spread is crucial for future epidemic modelling and management. In this study, seasonal variation in the transmission rate of covid-19, was estimated based on an epidemic population model of covid-19 in Denmark, which included changes in national restrictions and introduction of the $\alpha$-variant covid-19 strain, in the period March 2020 - March 2021. Seasonal variation was implemented as a logistic temperature dependent scaling of the transmission rate, and parameters for the logistic relationship was estimated through rejection-based approximate bayesian computation (ABC). The likelihoods used in the ABC were based on national hospital admission data and seroprevalence data stratified into nine and two age groups, respectively. The seasonally induced reduction in the transmission rate of covid-19 in Denmark was estimated to be $27\%$, (95% CI [$24\%$; $31\%$]), when moving from peak Winter to peak Summer. The reducing effect of seasonality on transmission rate per $+1{}^{\circ }$C in daily average temperature were shown to vary based on temperature, and were estimated to be $-2.2\%[-2.8\%;-1.7\%]$ pr. 1 ${}^{\circ }$C around $2^{\circ }$C; $2\%[-2.3\%;-1.7\%]$ pr. 1 ${}^{\circ }$C around $7{}^{\circ }$C; and $1.7\%[-2.0\%;-1.5\%]$ pr. 1 ${}^{\circ }$C around a daily average temperature of 11 ${}^{\circ }$C.

Extended-spectrum-β-lactamase-producing Escherichia coli (ESBL-EC) is a major public health concern. A better understanding of the dynamics of ESBL-EC transmission is required for effective prevention and control. We present here a multidirectional dynamic risk model for ESBL-EC transmission between broiler flocks, broiler farmers, and the open community, parameterized for the Netherlands. A discrete-time model was used to describe the transmission of ESBL-EC within and between populations including modeling the flock-to-human transmission via food consumption due to contamination at the slaughterhouse and/or during food preparation. The ESBL-EC prevalence reached an equilibrium prevalence of 0.65%, 24.7%, and 15.9% in the open community, farmers, and broiler flocks, respectively. The colonization of the open community could primarily be attributed to the open community itself (62%), followed by vegetable consumption (29.5%), and contact with farmers (8.5%). Model results were most sensitive to the estimated colonization and decolonization rate for humans. What-if analysis to explore the effect of interventions in the food production chain (i.e. from farm to fork) on the ESBL-EC prevalence in the open community indicated that interventions aimed at reducing the spread of ESBL-EC within broiler flocks were most effective. Interventions in the consumer phase (reduced cross-contamination in the kitchen, and reduced chicken meat consumption) resulted in a slightly lower ESBL-EC prevalence in the open community. Reducing cross-contamination at the slaughterhouse or reducing the proportion of broiler flocks with high antimicrobial use hardly had any effect on the prevalence in the open community. These results illustrate the relevance of the model for supporting the development of antimicrobial resistance risk mitigation strategies as part of public health policy making.

This paper reports, for the first time, standard Gibbs energies of binding of the BA.1, BA.2, BA.3, BA.2.13, BA.2.12.1 and BA.4 Omicron variants of SARS-CoV-2, to the Human ACE2 receptor. Variants BA.1 through BA.3 exhibit a trend of decreasing standard Gibbs energy of binding and hence increased infectivity. The BA.4 variant exhibits a less negative standard Gibbs energy of binding, but also more efficient evasion of the immune response. Therefore, it was concluded that all the analyzed strains evolve in accordance with expectations of the theory of evolution, albeit using different strategies.

During the COVID-19 pandemic, many statistical and epidemiological studies have been published, trying to predict the future development of the SARS-CoV-2 pandemic. However, it would be beneficial to have a specific, mechanistic biophysical model, based on the driving forces of processes performed during virus-host interactions and fundamental laws of nature, allowing prediction of future evolution of SARS-CoV-2 and other viruses. In this paper, an attempt was made to predict the development of the pandemic, based on biothermodynamic parameters: Gibbs energy of binding and Gibbs energy of growth. Based on analysis of biothermodynamic parameters of various variants of SARS-CoV-2, SARS-CoV and MERS-CoV that appeared during evolution, an attempt was made to predict the future directions of evolution of SARS-CoV-2 and potential occurrence of new strains that could lead to new pandemic waves. Possible new mutations that could appear in the future could lead to changes in chemical composition, biothermodynamic properties (driving forces of new virus strains) and biological properties of SARS CoV-2 that represent a risk for humanity.

The use of reclaimed water for irrigation is one of the most common strategies to address water scarcity in many regions of the world, and many of the most intensive production areas of fruits and vegetables rely on these water sources to produce high quality fresh produce. However, there are still concerns regarding the microbiological quality and safety of products irrigated with reclaimed water. In this study, we propose an innovative approach to evaluate factors affecting this potential risk. Using the concentration of Escherichia coli as a proxy (an indicator) for bacterial pathogens, we define a probabilistic model divided in two parts. The variation in bacterial concentration during water reclamation and distribution is described by a Bayesian Network, where variability and uncertainty are included by data augmentation using non-parametric bootstrap. The second part, is a stochastic model that predicts the microbial concentration on the plant accounting for cross-contamination and bacterial survival.

The novel approach is used to evaluate the factors affecting the contamination and potential risk associated with the consumption of leafy greens irrigated with reclaimed water from two wastewater treatment plants (WWTP) in several growing fields located in the south-east of Spain. According to the model, the microbial concentration in the outlet of the WWTP has a relatively low impact on the probability of E. coli concentrations on the plant to exceed 2 log CFU/g (a common threshold), and the impact of the irrigation system (overhead, drip or irrigation) would be insignificant. Instead, the probability of exceedance would be dominated by soil-to-plant contamination due to splashing, when organic amendments are used as fertilizers. Therefore, provided every step in water reclamation from water generation to point of use is kept safe, current reclamation treatments from WWTPs would be effective in reducing microbial concentrations in reclaimed water.