Microbial Risk Analysis最新文献

Aujeszky's disease (AD) is a highly contagious disease of pigs that primarily transmits by respiratory and oral routes. Evidence from recent outbreaks suggests that some swine viruses can survive in contaminated animal feed, thus posing a risk of entry via imports from other countries. To this end, a qualitative risk assessment was undertaken to determine the risk of introduction of AD virus (ADV) and infection of pigs via this route to determine if contaminated animal feed is a viable pathway for the spread of ADV. The feed categories investigated were soya bean/meal/oilcake, pet food, choline/lysine and spray dried porcine plasma. These were chosen based on their use in animal feed and the available data on viral contamination. The overall probability of an animal becoming infected from the importation of feed contaminated with ADV was estimated as Negligible or Very Low for all feed categories. The uncertainty associated with the estimates was assessed as Medium, due to the lack of data around the mechanisms that ADV could contaminate feedstuffs and for infection of susceptible animals from ADV infected feed.

Host-associated fecal indicator measurements can be coupled with quantitative microbial risk assessment to develop risk-based thresholds for recreational use of potential sewage-contaminated waters. These assessments require information on the relative concentrations of indicators and pathogens in discharged sewage, typically based on data collected from wastewater treatment plant influent samples. However, most untreated sewage releases occur from within the collection system itself (i.e. compromised sewer laterals, compromised gravity and force mains, sanitary sewer overflows), where these relationships may differ. This study therefore analyzed the concentrations of a selected reference pathogen (norovirus) and fecal indicator (HF183) in sewage samples from upper and lower segments of gravity sewage collection systems, wastewater pumpstations, and the influent and effluent of treatment plants, to characterize variability in their relative concentrations. Norovirus detection rates were lower and more variable in upper collection system samples due to the smaller population represented; whereas, HF183 was routinely detected at all sites with higher concentrations in the collection system compared to treatment plant influent, resulting in variable comparative relationships across sample locations (types). Mean HF183:NoV ratios ranged from 1.0 × 10⁵ for sewer lateral samples to 7 × 10° for force main samples. Results were used to develop risk-based thresholds for HF183 based on estimated recreational exposure to norovirus following a release from each potential sewage source, with higher thresholds for treatment facility influent compared to forced mains, or effluent. Consequently, this approach can allow for the rapid application of potential risk-based thresholds for recreational water quality applications based on different types of sewage discharge events.

Food safety has benefited from systematic approaches to assess and control risks. The paradigm of risk analysis calls for the components of risk assessment and risk management to be bridged and complemented with risk communication, yet still be separate activities. In practical terms, risk assessment and risk management are, in fact, heavily interdependent upon one another. Collectively, risk assessments, risk management and risk communications are tools or processes that deliver specific outputs. For food safety enhancement, these outputs must be translated into outcomes to yield the desired impacts—improved food safety, human health, and livelihoods. The purpose of this paper to illustrate, using the example of listeriosis, how steps in the risk analysis process used by the Codex Alimentarius Commission's, Codex Committee on Food Hygiene (CCFH), of the and the FAO/WHO Joint Expert Meeting on Microbiological Risk Assessment (JEMRA) align with the various components of the theory change, ultimately leading to impacts on food safety, enhanced health and livelihoods on the global scale.

Dairy farms are vulnerable to the impacts of climate change through the alteration of raw milk quality and pressure on animal health. Measures in dairy farms are necessary to reduce microbiological risks that may impact animal health and may be passed on to humans through the consumption of contaminated dairy products. However, these additional controls should incur lower environmental impact, have a low cost of implementation, minimal impact on milk property, and sufficient effectiveness to control risks. This study selected a dairy farm located under hot weather conditions to demonstrate how these challenges may be considered. Our objective was to present how a multi-criteria decision analysis (MCDA) could be used to select the appropriate mitigation strategy for this farm among fifteen potential food safety measures.

The MCDA framework brought together ten criteria classified into four supra criteria: stakeholder acceptance, food safety effectiveness, environmental impact, and impact on milk properties. The relative performances of various food safety measures scored against the ten criteria were expressed either in qualitative or quantitative values. Ultimately, the outranking MCDA technique, PROMETHEE II, was used to rank the measures.

This study ranked the selected four food safety measures, namely, sand bedding, chitosan supplementation, cooling mister operation, and phage spray, after a series of preselection filters. It was found that none of these dominated the others on the ten criteria. However, MCDA has allowed the determination of the best compromise among the selected measures. It was found that an increase in the frequency of changing the sand bedding ranked first, and an increase in the operation of cooling misters was ranked last.

The study demonstrated the benefit of MCDA in combining criteria of different nature (stakeholder acceptance, food safety effectiveness, environmental impact, milk properties), values, and scales to prioritize food safety measures. The approach can be applied to other dairy farms eager to limit the impacts of climate change while guaranteeing food safety.

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.