Microbial Risk Analysis最新文献

Human and animal feces are one of the main pollutants in drinking water systems (DWS). Both sources of fecal pollution are related to environmental conditions, such as poor land use management and little micro-basin protection. Cryptosporidium sp. and Giardia sp. are zoonotic protozoan water and foodborne transmitted parasitic pathogens and a frequent cause of diarrhea in children in low- and middle-income countries. In Latin America, DWS microbial risk assessment of these parasites is scarce. The aim of this study was to apply land use analysis, quantitative microbial risk assessment (QMRA), and disease burden (DALY; disability-adjusted life years) estimation of Cryptosporidium sp. and Giardia sp. for DWS that supplies 1.4% of the Costa Rican population. Land use analysis showed pollution sources and urban activities at the catchment area of all micro-basins, demonstrating the pollution's vulnerability, especially near to catchment sites of the DWS. The risk of infection by Cryptosporidium sp. and Giardia sp. was higher than US EPA standards, and the health burdens were above WHO recommendations. The higher risk of infection was observed in the micro-basins with the higher area of urban use, mainly in the buffer zones. QMRA and land use analysis are useful tools for the characterization of possible pollution foci in hazard identification.

Cross-contamination is an important event for bacterial transfer throughout the pork production chain. In Brazil, Salmonella sp. is the most relevant hazard in the pork industry, and further knowledge concerning its contamination is essential for in-depth risk assessments. Thus, we aimed to assess the transfer probability of Salmonella sp. between a knife and pork in a domestic kitchen scenario to provide parametrization for incorporating transfer of Salmonella sp. in risk assessment models. To estimate Salmonella Typhimuium transfer rates between contaminated pork and a knife blade during cutting, 23 independent experiments were performed. A Bayesian inference was utilized to determine the transfer probability, capturing the uncertainty generated in the transfer probability experiments. The mean transfer probability was 0.03 for knife to pork [0.029; 0.032] 95% credible interval (CrI) and 0.0042 for pork to knife [0.0041; 0.0043] 95% CrI. The probabilistic estimate of the transfer probability of Salmonella sp. during pork cutting gives insights on a relevant parameter for the consumer phase of the pork production industry in Brazil, allowing for enhanced risk assessment models.

This study was carried out to assess the bacteriological profile and to model the combined effects of physicochemical parameters on Escherichia coli ATCC 25922 levels in Amlou, a typical Moroccan traditional spread food made with argan oil, sweet almonds, and honey or sugar. Microbiological analyses of the samples (n = 44), collected from different traditional producers, included total viable count (TVC), total coliforms (TC), fecal coliforms (FC), E. coli, lactic acid bacteria (LAB), yeasts and molds (YM), Staphylococcus aureus, Salmonella, and Listeria spp. The results showed that 61% of samples met the Moroccan hygiene standards for vegetable paste spreads, with contamination levels of 4.21, 2.94, 2.38, 1.87, and 1.50 log cfu/g for TVC, YM, TC, FC, and E. coli, respectively. S. aureus was detected in 6.82% of Amlou samples, while Salmonella and Listeria spp. were not detected in any of the analyzed samples. On the other hand, the effects of water activity (a_w) (0.44, 0.50, 0.53), temperature (23, 30 °C), and pH (5.5, 6.5) on the kinetics parameters, inactivation rate (IR, log cfu/g/d), and shoulder period (SP, d) of E. coli ATCC 25922, were investigated. The survival curves generated under different conditions were fitted using the Baranyi model. Secondary modeling for the combined effects of a_w, temperature, and pH on the survival parameters was carried out using a polynomial equation. Finally, the goodness of fit was assessed for the survival kinetics of E. coli. Root mean square error (RMSE) and standard error of prediction (%SEP) obtained were 0.20 and 10.22% for IR and 0.24 and 40.01% for SP. These models can provide an estimate of E. coli inactivation in Amlou. Further studies should consider other factors such as argan oil concentration and amount of protein before being applied to ensure food safety for E. coli control in Amlou.

Campylobacter cross-contamination of Danish broiler flocks at slaughterhouses was investigated using data from two national surveillance components and from ad-hoc sampling. The animal level (AL) and food safety (FS) components from 2018 were compared. The AL component contained results of PCR on pools of cloacal swabs from 3,012 flocks processed at two Danish slaughterhouses (S1-S2), while the FS component regarded culture testing of leg skins from 999/3,012 flocks. The monthly “apparent” (AP) and “true” flock prevalence (TP) were estimated. Agreement between components was measured in percentage and in weighted-Kappa values. The relationship between the occurrence of cross-contamination (flock positive only in the FS component = cross-contaminated or CC, vs. flock negative in both components or NegBoth), slaughterhouse and surveillance period (quarter: Q1 to Q4) was evaluated by a generalized linear mixed effects (GLM) model. Thereafter, a linear mixed effects (LME) model was used to investigate the relationship between the level of meat contamination of carcass positive flocks (y = log10 colony forming units per gram, cfu/g), slaughterhouse, surveillance period, and flock type (CC vs. positive in both components or PosBoth). For both models, the farm was the random effect. Finally, in autumn 2019, ad-hoc field investigations were carried out testing caecal and neck skin samples, from two consecutive flocks at S1 and S2. Whole genome sequencing (WGS) was performed on isolates, for multilocus sequence typing (MLST) and single nucleotide polymorphisms (SNP) analysis. The monthly TP was always higher for the FS than for the AL component. Agreement between the components was substantial, but 8.1–8.6% of the flocks were CC. Those had median cfu/g 21–28 times lower than that of PosBoth flocks. In the GLM model, the explanatory variables were both significant (P-value <0.05). For example, the odds ratios (ORs) were 8.4 (95% CI: 4.0; 17.6) for Q3 vs. Q1, and 3.1 (1.8; 5.2) for S2 vs. S1. In the LME model, the flock type and the interaction between the other two variables, were significant. In the field study, a caecal positive flock was succeeded by an initially negative flock, in one out of five sampling sessions at S2. The cecal negative flock was positive in 58.3% of the neck skins with the isolate genetically similar to that from the caecal positive flock. Those results show that cross-contamination can be affected by surveillance periods and slaughterhouses, and it can contribute significantly to the TP of carcass positive flocks.

Since the beginning of the COVID-19 pandemic, SARS-CoV-2 has mutated several times into new strains, with an increased infectivity. Infectivity of SARS-CoV-2 strains depends on binding affinity of the virus to its host cell receptor. In this paper, we quantified the binding affinity using Gibbs energy of binding and analyzed the competition between SARS-CoV-2 strains as an interference phenomenon. Gibbs energies of binding were calculated for several SARS-SoV-2 strains, including Hu-1 (wild type), B.1.1.7 (alpha), B.1.351 (beta), P.1 (Gamma), B.1.36 and B.1.617 (Delta). The least negative Gibbs energy of binding is that of Hu-1 strain, -37.97 kJ/mol. On the other hand, the most negative Gibbs energy of binding is that of the Delta strain, -49.50 kJ/mol. We used the more negative Gibbs energy of binding to explain the increased infectivity of newer SARS-CoV-2 strains compared to the wild type. Gibbs energies of binding was found to decrease chronologically, with appearance of new strains. The ratio of Gibbs energies of binding of mutated strains and wild type was used to define a susceptibility coefficient, which is an indicator of viral interference, where a virus can prevent or partially inhibit infection with another virus.

Thermodynamic equilibrium models predict the infectivity of novel and emerging viruses using molecular data including the binding affinity of the virus to the host cell (as represented by the association constant K_{a_virus_T}) and the probability, p_virogenesis, of the virus replicating after entry to the cell. Here those models are adapted based on the principles of ligand binding to macromolecules to assess the effect on virus infectivity of inhibitor molecules which target specific proteins of the virus. Three types of inhibitor are considered using the thermodynamic equilibrium model for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection of the human lung with parameters for the strength and nature of the interaction between the target virus protein and the inhibitor molecule. The first is competitive inhibition of the SARS-CoV-2 spike glycoprotein (SGP) trimer binding to its human angiotensin converting enzyme 2 (ACE2) receptor by unfractionated heparin (UFH). Using a novel approach presented here, a value of K_{a_virus_T} = 3.53 × 10¹⁷ M⁻¹ is calculated for SARS-CoV-2 from the IC₅₀ for inhibition by UFH of SARS-CoV-2 plaque formation in cell culture together with the dissociation constant K_VI of 0.73 × 10⁻¹⁰ M reported for heparin binding to SARS-CoV-2 SGP trimer. Such a high K_{a_virus_T} limits the effectiveness of competitive inhibitors such as UFH. The second is the attachment of a nanoparticle such as a zinc oxide tetrapod (ZnOT) to the virus shell as for herpes simplex virus (HSV). The increase in molecular weight through ZnOT attachment is predicted to decrease K_{a_virus_T} by orders of magnitude by making the entropy change (ΔS_{a_immob}) on immobilisation of the ZnOT:virus complex on cell binding more negative than for the virus alone. According to the model, ZnOT acts synergistically with UFH at the IC₅₀ of 33 μg/cm³ which together decrease viral infectivity by 61,000-fold compared to the two-fold and three-fold decreases predicted for UFH alone at the IC₅₀ and for ZnOT alone respectively. According to the model here, UFH alone at its peak deliverable dose to the lung of 1,000 μg/cm³ only decreases infectivity by 31-fold. Practicable approaches to target and decrease ΔS_{a_immob} for respiratory viruses should therefore be considered. The combination of decreasing ΔS_{a_immob} together with blocking the interaction of virus surface protein with its host cell receptor may achieve synergistic effects for faecal-oral viruses and HSV. The third is reversible noncompetitive inhibition of the viral main protease (M^pro) for which the decrease in p_virogenesis is assumed to be proportional to the decrease in enzyme activity as predicted by enzyme kinetic equations for a given concentration of inhibitor which binds to M^pro