Microbial Risk Analysis最新文献

The Food Safety Knowledge Exchange (FSKX) format is a community-driven effort initially created to promote the efficient exchange of data and models in the food safety domain. Over the past years this effort was driven by the Risk Assessment Knowledge Integration Platform (RAKIP) Initiative that also provided a number of software tools and FSKX-compliant model files via their website https://foodrisklabs.bfr.bund.de/rakip-initiative/.

This paper describes the results of a SWOT analysis that was conducted to identify strategic avenues for enhancing FSKX's usability and adoption. The SWOT analysis identified a number of recommendations for the future evolution of FSKX. First, it is recommended to reduce the complexity of the annotation schema to ease the adoption of the format. Second, a clear distinction between the descriptive part of FSKX and the executable part is proposed. To promote the broad usage of FSKX-compliant models, it is also recommended to develop and provide FSKX-compliant APIs and resources that facilitate cloud-based execution.

As part of the research to prioritize future FSKX development options, we also considered the implications of the emerging generative AI technologies, particularly which impact large language models (LLMs) might have in supporting the adoption of FSKX by the research community. Recognizing the format's application potential beyond the food safety domain, we then proposed to re-brand the FSKX acronym as "FAIR Scientific Knowledge Exchange Format" which better reflects its broad applicability in various scientific domains. Our research findings suggest that with the implementation of the improvements identified by the SWOT analysis and the broader availability of generative AI technologies the broad adoption of FSKX as a method to share data and models in a FAIR way comes into reach.

This work focuses on the effects of temperature, pH, water activity, and concentrations of acetic or lactic acid on the growth limits of psychrotrophic Bacillus cereus sensu lato (s.l.). A previously published growth boundary model, based on an ‘interaction term’, was extended by the integration of new environmental factors. Further development has been made by replacing, wherever possible, the single values for strain-dependent parameters by statistical distributions, making it possible to describe the intra-group variability in B. cereus s.l. behaviour. The parameters associated with organic acid (i.e., the Minimum Inhibitory Concentrations, MIC) were determined for one strain for lactic acid and three strains for acetic acid. The MICs estimated were close to previously published values for mesophilic reference group III strain F4810/72. The growth/ no growth interface for psychrotrophic B. cereus s.l. in absence of organic acid was defined by the lower growth limits obtained separately for “groups II and V” and “group VI”. The model predictions for the transition between the “no-growth only” and “possible growth” provide fail-safe predictions for ComBase and literature data (468 records). To investigate behaviour of psychrotrophic B. cereus s.l. under organic acid, growth/ no growth data were generated at 15 °C (simulating mild temperature abuse) for three B. cereus s.l. strains (one from group II and two from group VI) at different pH levels (between 4.8 and 6.2), water activities (between 0.974 and 0.997) and concentrations of acetic acid (up to 45 mM) or lactic acid (up to 100 mM). Each of the three strains was studied separately for a total of 312 experiments. The minimum pH levels required for growth increase in the presence of lactic or acetic acid, highlighting their inhibitory effects. These inhibitory effects are enhanced at the lowest water activity tested. Although, group VI strains were reported to be more affected by low a_w, only small differences were observed between group II and group VI at a_w 0.974. The developed model was found to provide conservative (i.e. fail-safe) predictions for the growth limits under acetic or lactic acid at 15 °C.

This study aims to develop and evaluate a predictive model for Shiga toxin-producing Escherichia coli (STEC) growth on Minas Frescal cheese across varied temperature conditions. A pool of five STEC strains (3–4 log CFU/g) was inoculated onto 10 g Minas Frescal cheese portions (%moisture = 68.30 ± 0.47,%fat in dry basis = 26.55 ± 0.37, pH = 6.86 ± 0.02) stored at isothermal conditions (4, 8, 15, 25, 37, and 42 °C). STEC concentrations increased at 8 °C and above, persisting throughout the 504-hour study period at 4 °C, showing minimal cell loss. The growth curves were fitted with the primary model of Baranyi and Roberts using Combase DMFit, showcasing robust alignment between predicted and experimental data (R² ≥ 0.98). Further, the µ_max and λ values were fitted as a function of temperature to modified Ratkowsky equations, resulting in R² of 0.99 and 0.96, and RMSE of 0.03 and 0.08, respectively, for the secondary models. Model validation was performed under isothermal conditions at 20 and 30 °C. The Ratkowsky equations can reliably predict STEC growth rate and lag phase in Minas Frescal cheese at diverse temperatures (8 to 42 °C), evidenced by accuracy and bias factors of 1.06 and 1.06. These findings offer insights into cold chain management for STEC control during Minas Frescal cheese production, distribution, and storage, emphasizing the need for robust post-pasteurization manufacturing practices to prevent STEC survival even at lower temperatures.

Introduction

Brucellosis is rare in non-endemic countries where it mainly occurs as an imported or travel-related disease. In rare cases, Brucella species (spp.) are present in clinical specimens processed by clinical microbiology laboratories. These pathogens pose a risk to laboratory technicians, due to the high virulence, a low-infectious dose and ease of aerosol formation. Due to the low incidence in non-endemic countries, clinical samples are routinely processed on laboratory benches outside laminar flow cabinets. Recently, we have had three unexpected cases in which Brucella spp. were cultured at our clinical microbiology laboratory: one Brucella canis case and two Brucella melitenis cases. The B. canis and the first B. melitenis cases prompted the introduction of a biosafety software pop-up, which is presented in this paper.

Methods

Here, we describe the two B. melitensis cases and the introduction of a biosafety pop-up. The software pop-up parameters are a time-to-positivity (TTP) of more than 48 h, in an aerobic blood culture bottle, and a Gram stain appearance as Gram-negative bacteria. The software pop-up warns the technician through the laboratory information system (LIS) to further process the specimen in the Class 2 biological safety cabinet. To assess the number of false-positive pop-ups we can expect and resulting additional workload, we retrospectively analyzed laboratory data from the last seven years.

Results

The biosafety pop-up prevented laboratory exposure in the second B. melitensis case. Based on the retrospective analysis of laboratory data, we estimated the resulting additional workload of implementation of the biosafety pop-up to be less than one blood culture bottle per week on average to be processed in a Class 2 biological safety cabinet.

Conclusion

Our experience demonstrates that implementation of the biosafety software pop-up can reduce the risk of laboratory exposure to Brucella spp. This intervention provides a feasible approach even in a setting where Brucella spp. are normally only encountered every few years.

A rapid and nondestructive assessment of food safety risk using machine learning-assisted hyperspectral imaging was developed for classification of fungal contamination in brown rice grain. Brown rice was inoculated with Penicillium. The fungal infected rice was then mixed with healthy rice to obtain 0 %, 5 %, 25 %, 50 % and 100 % (w/w) contamination of infected rice. Volatile compounds including pentamethyl-heptane, decane, dodecane, 3-octanone, and 1-octen-3-ol were found in fungal infected rice, as analyzed using gas chromatography-mass spectrometry. The HSI system was used to collect spectral reflectance and spatial data of the samples covering the wavelength range of 400–1000 nm. The hypercubed data were analyzed using machine learning algorithms, including principal component analysis (PCA), discriminant factor analysis (DFA) and support vector machine (SVM). Using PCA for data reduction, 3 principal components were extracted with a cumulative variance of 90.53 %. DFA (linear and quadratic algorithms) and SVM (linear, quadratic, cubic, and Gaussian algorithms) were then used to classify the samples. HSI integrated with Gaussian SVM gave 93.4% accuracy which was best for classifying rice with different percentages of contamination. The image analysis gave a pseudo-color distribution map which facilitated the visualization of the contaminated rice by presenting data in an uncomplicated image. The machine learning-assisted HSI can be used as a rapid, nondestructive and chemical-free tool for an assessment of food safety risk for rice grain.

Clostridium perfringens has been implicated in food poisoning outbreaks linked to cooked cured meat. Although there are regulatory requirements to prevent its growth during meat production, additional control measures may reduce the C. perfringens risk. This study examined the effect of sodium chloride (salt) and sodium tripolyphosphate (STPP) on the growth probability of C. perfringens in a cooked cured meat. Ground beef (10 % fat) was mixed with 200 ppm sodium nitrite, 1–4 % salt, and 0–1.5 % STPP and inoculated with C. perfringens spores. Five grams of meat were vacuum-packaged in individual bags and heated at 70 °C for 30 min to activate the spores. Ten bags from each formulation were incubated at 46 °C for 48 h. The populations of C. perfringens before and after incubation were enumerated to determine the growth event of C. perfringens (an increase of >1.0 log CFU/g population after incubation) for each sample. The growth event ratios were fitted with a logistic model to develop a C. perfringens growth probability model as a function of the concentrations of salt and STPP. The combinations of 1 % salt and up to 1.5 % STPP were not able to prevent the growth of C. perfringens. For 2, 3, and 4 % salt, the growth/no growth boundaries were observed at approximately 1.5, 1.0, and 0.5 % STPP, respectively. The resulting model indicates that salt and STPP were significant factors (p < 0.05) affecting the growth probability of C. perfringens. This study identified the concentrations of salt and STPP that prevent the growth of C. perfringens in a cooked cured meat containing 200 ppm sodium nitrite. The model could be used for predicting the growth probability of C. perfringens as affected by salt and STPP concentrations and for selecting the additive concentrations that may reduce the growth probability of C. perfringens in cooked cured meat products.

Pertussis (whooping cough) has been nearly eradicated during the 20th century, first of all due to an organized and comprehensive vaccination campaign that lasted for decades. Generations of doctors educated in Serbia (and other countries) rarely had an opportunity to see the clinical picture of pertussis. However, during 2023, the number of registered cases of pertussis in Serbia has increased several times. This is why the health authorities were forced to declare danger of an epidemic. During 2023, in Belgrade, around 1000 cases were registered. During the two months of 2024, 400 cases were registered. Some of them have ended with lethal outcome. This paper reports for the first time the biosynthesis reaction and thermodynamic properties of biosynthesis (enthalpy, entropy and Gibbs energy) of Bordetella pertussis, the cause of whooping cough. Moreover, a mechanistic model of multiplication of B. pertussis was developed. The mechanistic model was related to the pathogenesis of pertussis.

In the past two years, Covid-19 has emerged as the most severe and pressing public health issue, causing a great deal of damage to societal and economic welfare, as well as causing illness and mortality. The operators in wastewater treatment plants (WWTPs), particularly those employed in rural communities, appear to often exhibit a lack of adherence to proper safety protocols by not utilizing sufficient protective equipment while handling unprocessed sewage samples throughout the different phases of wastewater treatment and disposal. This study aimed at examining the potential health risk of infection among WWTP operators, as a result of unintentional ingestion of wastewater during routine duties in facilities that receive influent containing Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) from various areas. This study examined the prevalence of SARS-CoV-2 in grab samples of untreated wastewater samples using the real-time quantitative polymerase chain reaction (RT-qPCR) technique and quantitative microbial risk assessment (QMRA) model was employed on three probable exposure of SARS-CoV-2 scenarios that are expressed as moderate, aggressive and extreme (2 mL, 10 mL, 20 mL) to evaluate the probability of infection to WWTP workers based on the 6 h that the workers spent in WWTPs performing their daily activities which exposed them to potential health risk of various pathogens. At the highest SARS-CoV-2 genome of 266.23 × 10² gc/mL, the findings indicated that there was no statistically significant difference in the probability of infections with respect to seasonal differences because the P(i) value was greater than 0.05 (p > 0.05). Overall, P(i) was highly significant across all volumetric scenarios in the study with p value that was p < 0.001. The probability of getting infected during the different seasons is assumed to be low since there was no statistically difference in P(i) with respect to season however it can be assumed that there is a high chance of getting infected regardless of volumetric intake. Our study suggests that the risk of accidental occupational exposure to SARS-CoV-2 in raw wastewater is negligible to workers whereby workers would perform their daily activities without wearing protective gear. Nevertheless, the importance and work of WWTPs by workers should not be overlooked. Regardless of the situation, it is widely recognized that residential wastewater poses a potential risk of infection due to the presence of several enteric pathogens, therefore, it is crucial to ensure that those who are occupationally exposed to untreated wastewater are well equipped with suitable personal protective equipment (PPE).

Greywater reuse is a strategy to address water scarcity, necessitating the selection of treatment processes that balance cost-efficiency and human health risks. A key aspect in evaluating these risks is understanding pathogen contamination levels in greywater, a complex task due to intermittent pathogen occurrences. To address this, faecal indicator organisms like E. coli are often monitored as proxies to evaluate faecal contamination levels and infer pathogen concentrations. However, the wide variability in faecal indicator concentrations poses challenges in their modelling for quantitative microbial risk assessment (QMRA). Our study critically assesses the adequacy of parametric models in predicting the variability in E. coli concentrations in greywater. We found that models that build on summary statistics, like medians and standard deviations, can substantially underestimate the variability in E. coli concentrations. More appropriate models may provide more accurate estimations of, and uncertainty around, peak E. coli concentrations. To demonstrate this, a Poisson lognormal distribution model is fit to a data set of E. coli concentrations measured in shower and laundry greywater sources. This model estimated arithmetic mean E. coli concentrations in laundry waters at approximately 1.0E + 06 MPN 100 mL⁻¹. These results are around 2.0 log₁₀ units higher than estimations from a previously used hierarchical lognormal model based on aggregated summary data from multiple studies. Such differences are considerable when assessing human health risks and setting pathogen reduction targets for greywater reuse. This research highlights the importance of making raw monitoring data available for more accurate statistical evaluations than those based on summary statistics. It also emphasizes the crucial role of model comparison, selection, and validation to inform policy-relevant outcomes.

Survival curves of bacterial vegetative cells or spores subjected to an inactivation process are often log-linear and then described by the d-value parameter. However, non log-linear, convex, shapes might be also observed particularly when mild inactivation treatments are applied. Our objective was to investigate whether the 3-parameters Weibull model (logN₀, $\delta$, p) could be used to go beyond a simple fitting of convex curve by providing information related to bacterial variability. First, survival curves were simulated to mimic the behaviour of a cocktail containing bacterial vegetative cells or spores undergoing an inactivation treatment, on which the Weibull model was fitted. Second, a mathematical model was developed to describe the link between the Weibull parameters p and delta with the d-values of sub-populations of bacterial vegetative cells or spores (considering as well the percentage of each sub-population). Based on this model, it was shown that the Weibull model can be used to go beyond a simple description of a convex curve. For instance, if p is estimated around 0.8, that means the presence of a resistant sub-population, but with a limited resistant variability (ratio of resistance from 1.5 to 4). In contrast, if p is estimated to 0.3–04 that means the presence of a resistant sub-population in a small proportion (less than 10 %) combined with a large resistant variability (ratio of 10 or more). This study shows that the Weibull model can be used in combination with the new model developed here to decipher vegetative cell or spore resistance variability, with application in food industry processes such as thermal or physical inactivation treatment as well as cleaning and disinfection verification procedure.