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The application of high-throughput DNA sequencing technologies (WGS) data remain an increasingly discussed but vastly unexplored resource in the public health domain of quantitative microbial risk assessment (QMRA). This is due to challenges including high dimensionality of WGS data and heterogeneity of microbial growth phenotype data. This study provides an innovative approach for modeling the impact of population heterogeneity in microbial phenotypic stress response and integrates this into predictive models inputting a high-dimensional WGS data for increased precision exposure assessment using an example of Listeria monocytogenes. Finite mixture models were used to distinguish the number of sub-populations for each of the stress phenotypes, acid, cold, salt and desiccation. Machine learning predictive models were selected from six algorithms by inputting WGS data to predict the sub-population membership of new strains with unknown stress response data. An example QMRA was conducted for cultured milk products using the strains of unknown stress phenotype to illustrate the significance of the findings of this study. Increased resistance to stress conditions leads to increased growth, the likelihood of higher exposure and probability of illness. Neglecting within-species genetic and phenotypic heterogeneity in microbial stress response may over or underestimate microbial exposure and eventual risk during QMRA. ispartof: MICROORGANISMS vol:8 issue:11 ispartof: location:Switzerland status: published

Nisin is a commonly used bacteriocin for controlling spoilage and pathogenic bacteria in food products. Strains possessing high natural nisin resistance that reduce or increase the potency of this bacteriocin against Listeria monocytogenes have been described. Our study sought to gather more insights into nisin resistance mechanisms in natural L. monocytogenes populations by examining a collection of 356 field strains that were isolated from different foods, food production environments, animals and human infections. A growth curve analysis-based approach was used to access nisin inhibition levels and assign the L. monocytogenes strains into three nisin response phenotypic categories; resistant (66%), intermediate (26%), and sensitive (8%). Using this categorization isolation source, serotype, genetic lineage, clonal complex (CC) and strain-dependent natural variation in nisin phenotypic resistance among L. monocytogenes field strains was revealed. Whole genome sequence analysis and comparison of high nisin resistant and sensitive strains led to the identification of new naturally occurring mutations in nisin response genes associated with increased nisin resistance and sensitivity in this bacterium. Increased nisin resistance was detected in strains harboring RsbUG77S and PBPB3V240F amino acid substitution mutations, which also showed increased detergent stress resistance as well as increased virulence in a zebra fish infection model. On the other hand, increased natural nisin sensitivity was detected among strains with mutations in sigB, vir, and dlt operons that also showed increased lysozyme sensitivity and lower virulence. Overall, our study identified naturally selected mutations involving pbpB3 (lm0441) as well as sigB, vir, and dlt operon genes that are associated with intrinsic nisin resistance in L. monocytogenes field strains recovered from various food and human associated sources. Finally, we show that combining growth parameter-based phenotypic analysis and genome sequencing is an effective approach that can be useful for the identification of novel nisin response associated genetic variants among L. monocytogenes field strains.

The accurate identification of Extended-Spectrum &beta, Lactamase (ESBL) genes in Gram-negative bacteria is necessary for surveillance and epidemiological studies of transmission through foods. We report a novel rapid, cheap, and accurate closed tube molecular diagnostic tool based on two multiplex HRM protocols for analysis of the predominant ESBL families encountered in foods. The first multiplex PCR assay targeted blaCTX-M including phylogenetic groups 1 (CTX-M-1-15, including CTX-M-1, CTX-M-3 and CTX-M-15), 2 (CTX-M-2), and 9 (CTX-M-9-14, including CTX-M-9 and CTX-M-14). The second assay involved blaTEM /bla CTX-M /blaSHV, including TEM variants (TEM-1 and TEM-2), SHV-1-56 (SHV-1, SHV-2 and SHV-56), and CTX-M-8-41 (CTX-M-8, CTX-M-25, CTX-M-26 and CTX-M-39 to CTX-M-41). The individual melting curves were differentiated by a temperature shift according to the type of ESBL gene. The specificity and sensitivity of the first assay were 100% and 98%, respectively. For the second assay, the specificity and sensitivity were 87% and 89%, respectively. The detection of ESBL variants or mutations in existing genes was also demonstrated by the subtyping of a variant of the CTXM-1-15. The HRM is a potential tool for the rapid detection of present &beta, lactamase genes and their characterization in a highly sensitive, closed-tube, inexpensive method that is applicable in high throughput studies.

Camel milk provides a common source of nutrition but also a potential rich source of beneficial, pathogenic, and potentially pathogenic microorganisms. This chapter reviews lactic acid and probiotic bacteria from camel milk, product innovation using such bacteria, and potential areas of improvement in technical as well as practical aspects of fermentation technologies. Lactic acid bacteria fermentation helps mitigate the impact of poor handling and storage conditions by enhancing shelf life and food safety. Traditionally-fermented sour milk products are culturally accepted and widely distributed worldwide with product-specific microbiota responsible for aroma, flavor, and texture. Knowledge of microbiota and predominant, technologically important microorganisms associated with camel milk is critical in developing products with enhanced quality and safety, as well as sustainable interventions for these products, including camel milk specific starter culture development. This chapter presents occurrence of LAB and probiotic bacteria in camel milk and technological aspects of camel dairy.

The microbial contamination level profiles (MCLPs) attributed to contamination of beef carcasses, personnel, and equipment in five Kenyan small and medium enterprise slaughterhouses were determined. Aerobic plate counts, Enterobacteriaceae, Staphylococcus, and Salmonella were used to determine contamination at four different slaughter stages, namely, dehiding, evisceration, splitting, and dispatch. Microbiological criteria of the four microorganisms were used to score contamination levels (CLs) as poor (0), poor to average (1), average (2), or good (3). MCLPs were further assigned to carcasses, personnel, and equipment at each stage by summing up the CL scores. The CL score attributed to aerobic plate count contamination was 2 or 3 for carcasses but 0 for personnel and equipment in almost all slaughterhouses. A score of 0 on carcasses was mostly attributed to Enterobacteriaceae at evisceration and to Salmonella at dehiding and evisceration. In addition, a score of 0 was mostly attributed to Staphylococcus contamination of personnel at dehiding. A score of 3 was attributed mostly to Enterobacteriaceae on hands at splitting, whereas a score of 2 was mostly attributed to the clothes at dehiding and evisceration. A CL score of 3 was mostly attributed to Enterobacteriaceae and Salmonella contamination of equipment at dehiding and splitting, respectively. Although CLs attributed to contamination of carcasses, personnel, and equipment ranged from 0 to 3, the maximum MCLP score of 9 was only attained in carcasses from two slaughterhouses at dehiding and from one slaughterhouse at dispatch. There is, therefore, a lot of room for small and medium enterprise slaughterhouses to improve their food safety objectives by improving food safety management systems at the points characterized by low CL scores.

Fresh beef and meat products have been implicated in outbreaks of Shiga toxin-producing Escherichia coli (STEC) worldwide. This study investigated the prevalence of E. coli O157: H7 and non-O157 STEC serogroups in fresh beef in the open market and street vended meat products (n = 180) in Lagos metropolis, Nigeria. A combination of culture media and immunomagnetic separation followed by typing for associated virulence factors and serotypes was performed. Antimicrobial susceptibility testing was performed on the isolated STEC serotypes using the disk diffusion method. A total of 72 STEC serogroup isolates were detected from 61 out of 180 samples. The O157 STEC serotypes were detected in fresh beef, suya, minced meat and tsire with prevalence of 20.8% while non-O157 STEC serogroups were detected in all the samples. Molecular typing revealed 25% (n = 18) of the STEC serogroups showed presence of all the stx1, stx2, eaeA, fliCH7 and rfbEO157 virulence factors while 54.2% (n = 39) possessed a combination of two virulence genes. Multidrug resistance was discovered in 23.6% (n = 17) of the total STEC serogroups. Locally processed ready-to-eat meat products in Lagos metropolis, Nigeria harbour potentially pathogenic multi-drug resistant STEC serogroups that can constitute public health hazard.

We investigated for virulence genes ( stx1 , stx2 and hlyA ), serotypes and extended-spectrum β-lactamases (ESBLs) producing capacity in O157 and non-O157 Escherichia coli isolated from producer-distributor bulk milk (PDBM). Fifteen different E. coli O-serogroups were observed from the isolates (n = 121). The prevalence of stx1 and stx2 genes among the E. coli isolates was 8.3% and 11.6%, respectively, while 5.8% harboured both stx1 and stx2 . Four E. coli isolates (3.3%) had ESBLs producing capacity, resisted multiple cephalosporins and aztreonam, and carried stx genes. Cluster analysis using GTG 5 finger printing revealed a diversity of E. coli seropathotypes in PDBM that are known to be associated with human diarrhoeal diseases. These results highlight a potential risk posed on human health by the consumption of PDBM contaminated with pathogenic E. coli . A further quantitative risk assessment of the impact of pathogenic E. coli contamination in PDBM on human health is therefore recommended.

The contribution of the fresh produce production environment to human exposure with bacteria bearing extended spectrum β-lactamases and AmpC β-lactamases (ESBL/AmpC) has not been reported. High prevalence of ESBLs/AmpC bearing E. coli as well as a high gene transfer efficiency of lettuce and irrigation water E. coli isolates was previously reported. This stochastic modeling was aimed at quantitatively assessing human exposure to ESBL/AmpC bearing E. coli through lettuce attributable to irrigation water and subsequent horizontal gene transfer. Modular process risk approach was used for the quantitative exposure assessment and models were constructed in Ms. Excel spreadsheet with farm to consumption chain accounted for by primary production, processing, retail and consumer storage. Probability distributions were utilised to take into account the variability of the exposure estimates. Exposure resulting from ESBL/AmpC positive E. coli and gene transfer was taken into account. Monte Carlo simulation was carried out using @Risk software followed by sensitivity and scenario analysis to assess most effective single or combinations of mitigation strategies for the ESBL/AmpC positive E. coli events from farm to fork. Three percent of South African lettuce consumers are exposed to lettuce contaminated with about 106.4±106.7 (95% CI: 105.1-107) cfu of ESBL/AmpC positive E. coli per serving. The contribution of originally positive isolates and conjugative genetic transfer was 106±106.7 (95% CI: 105-107) and 105.2±105.6 (95% CI: 103.9-105.8) cfu per serving respectively. Proportion of ESBL/AmpC positive E. coli (Spearman's correlation coefficient (ρ)=0.85), conjugative gene transfer (ρ=0.05-0.14), washing in chlorine water (ρ=0.18), further rinsing (ρ=0.15), and prevalence of E. coli in irrigation water (ρ=0.16) had highest influence on consumer exposure. The most effective single methods in reducing consumer exposure were reduction in irrigation water microbial quality variation (87.4% reduction), storage period (49.9-87.4% reduction) and growth rate reduction by 75% (90% reduction). Reduction in growth rate together with storage time (92.1-99.4%) and reduction in storage time combined with E. coli concentration in irrigation water (95-96% reduction) were most effective combinations of mitigation measures. The high variation in exposure reflected the high irrigation water quality variation. The exposure levels may impose higher consumer risk than acceptable for irrigation water risk. E. coli contamination and growth related measures, as well as measures to reduce contamination with antimicrobial resistant E. coli from lettuce production environment are recommended. This exposure model could form a basis for the development of similar models assessing the impact of contaminated irrigation water and gene transfer in other microbial hazards, antimicrobial resistance types and fresh produce types.

The current study was undertaken to characterize Escherichia coli and other Enterobacteriaceae in raw and pasteurized producer-distributor bulk milk (PDBM). A total of 258 samples were collected from purchase points in 8 provinces in South Africa. The samples were tested for antibiotic residues, phosphatase, total aerobic bacteria, coliforms, and E. coli counts. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry was used for identification of isolates. Escherichia coli isolates were characterized for virulence factors, antimicrobial resistance, serotypes, and presumptive E. coli O157:H7. Antibiotic residues and alkaline phosphatase were detected in 2% of both raw and pasteurized PDBM (n=258) and 21% pasteurized PDBM (n=104) samples, respectively. A total of 729 isolates belonging to 21 genera and 59 species were identified. Escherichia coli, Enterobacter cloacae, Klebsiella oxytoca, and Raoultella ornithinolytica were the most abundant species. Spoilage Enterobacteriaceae species exceeded 50% of the total isolates. Escherichia coli was detected and isolated from 36% of the milk samples. Thirty-one E. coli isolates harbored virulence genes stx1/stx2 and 38% (n=121) were presumptive O157:H7. The prevalence of samples with presumptive shigatoxin producing E. coli was 10%. Antimicrobial-resistant E. coli isolates were detected in 70% of the milk samples with 36% of stx1/stx2 positive E. coli showing multi-drug resistance. Information obtained from the study will be used for modeling the public health risk posed by milkborne pathogens in PDBM, which in many cases is consumed by poor and vulnerable members of the population.

Next‐generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and virulence among strains. The potential of machine learning algorithms for predicting the risk/health burden at the population level while inputting large and complex NGS data was explored with Listeria monocytogenes as a case study. Listeria data consisted of a percentage similarity matrix from genome assemblies of 38 and 207 strains of clinical and food origin, respectively. Basic Local Alignment (BLAST) was used to align the assemblies against a database of 136 virulence and stress resistance genes. The outcome variable was frequency of illness, which is the percentage of reported cases associated with each strain. These frequency data were discretized into seven ordinal outcome categories and used for supervised machine learning and model selection from five ensemble algorithms. There was no significant difference in accuracy between the models, and support vector machine with linear kernel was chosen for further inference (accuracy of 89% [95% CI: 68%, 97%]). The virulence genes FAM002725, FAM002728, FAM002729, InlF, InlJ, Inlk, IisY, IisD, IisX, IisH, IisB, lmo2026, and FAM003296 were important predictors of higher frequency of illness. InlF was uniquely truncated in the sequence type 121 strains. Most important risk predictor genes occurred at highest prevalence among strains from ready‐to‐eat, dairy, and composite foods. We foresee that the findings and approaches described offer the potential for rethinking the current approaches in MRA.

This study evaluated the microbiological safety of fresh Nile tilapia ( Oreochromis niloticus) from Kenyan fresh water fish value chains. One hundred seventy-six fish samples were analyzed. The microbial counts of hygiene indicators, total viable aerobic count (TVC), total coliforms, and fecal coliforms isolated by using culture techniques were enumerated, and microbial pathogens present in the fish samples were identified and characterized by using molecular methods. The diversity of bacterial isolates was determined by using the Shannon-Weaver diversity index. The mean of TVC in the samples was 4.44 log CFU/g. A comparison with the European Commission and International Commission on Microbiological Specifications for Foods standards showed two fish samples had counts above the 5.00 log CFU/g limit for TVC, and all the fish samples had total coliform and fecal coliform counts above 2.00 and 1.00 log CFU/g, respectively. Pathogenic strains, including Shiga toxin-producing and enteropathogenic Escherichia coli, Listeria monocytogenes, Yersinia enterocolitica, Klebsiella pneumoniae, and Salmonella enterica, were identified in the fish samples. The diversity of 1,608 bacterial isolates was higher in semiregulated chains than unregulated chains. The diversity was also high at the retail stage of the fish value chain. In conclusion, fresh Nile tilapia samples were above some of the set food safety standards and may be a source of foodborne pathogens. Further microbial risk assessment for detected pathogens is recommended to further support public health protection, taking into account growth, inactivation through cooking, processing, survival, and consumption.

In this study, the bacteriological quality and the presence of Staphylococcus aureus, Escherichia coli O157:H7, Bacillus cereus, Salmonella species, and Pseudomonas species were studied in raw milk, cultured milk, milk handlers and packaging containers. A total of 36 samples were collected over 3 months from three different farmers. Samples were analyzed for means of counts per milliliter of milk for total bacterial count (TBC), total coliform count (TCC), total E. coli count (TEC), S. aureus, B. cereus, Salmonella spp. and Pseudomonas spp. Microbial load ranged between 0.81 and 7.6 log10 cfu/ml for various critical sampling locations. Isolates of E. coli, S. aureus and B. cereus were taken for simple polymerase chain reaction (PCR) to investigate the presence of virulent genes, rfB, sei, and cytK with amplicon sizes of 1.0 kb, 500 bp and 320 bp, respectively. The sei gene was detected in 19% of the samples and 2.8% were found to have the cytK gene. The rfB gene could not be picked in E. coli. The results show poor hygienic practices at the processors and potential risk to the consumers. Key words: Fresh milk, fermented milk, packaging container, milk handler, pathogens.

The present study aimed at identifying and assessing antimicrobial resistance of Enterococcus spp. isolated from small and medium enterprise slaughterhouses in Kenya. In total, 67 isolates were recovered from 48 of 195 samples examined from beef carcasses, personnel, and cutting equipment in five small and medium enterprise slaughterhouses. The isolates were identified by using matrix-assisted laser desorption-ionization time of flight mass spectrometry and screened thereafter for their resistance against 12 antibiotics by using a disk diffusion assay. The isolates ( n = 67) included Enterococcus faecalis (41.8%), Enterococcus mundtii (17.9%), Enterococcus thailandicus (13.4%), Enterococcus faecium (9.0%), Enterococcus hirae (7.5%), Enterococcus casseliflavus (6.0%), and Enterococcus devriesei (4.5%). None of the isolates were resistant to ciprofloxacin, penicillin, ampicillin, vancomycin, nitrofurantoin, teicoplanin, linezolid, and levofloxacin. Resistance to rifampin (46.3%), erythromycin (23.9%), tetracycline (20.9%), and chloramphenicol (7.5%) was distributed among six of the seven species. All E. thailandicus were resistant to rifampin, erythromycin, and tetracycline. E. faecalis was resistant to rifampin (60.7%), tetracycline (17.9%), erythromycin (14.3%), and chloramphenicol (10.7%). Resistance to two or three antibiotics was observed in 26.9% of the enterococci isolates. The isolation of enterococci that are resistant to clinically relevant antibiotics, such as erythromycin, is of a serious concern given the role enterococci play in the transfer of antibiotic resistance genes.

As more microbiological data for indigenous fermented milk (IFM) becomes available, concern about their microbial safety becomes eminent. Nonetheless, these data are highly fragmented, and a tool is required to integrate existing data and to provide a basis for data-driven decision making for IFM&rsquo, s safety. Therefore, meta-analysis and meta-regression were conducted to estimate the prevalence of foodborne pathogens in IFM and to determine factors influencing the estimated values. Using Africa as a case, searches were systematically made for published data and relevant grey literature. Data from 18 studies in 15 countries were analyzed. Staphylococcus aureus (37%), pathogenic Escherichia coli (16%), Listeria monocytogenes (6%), and Salmonella spp. (3%) were the most prevalent pathogens with a pooled prevalence estimate of 12%. Heterogeneity among prevalence estimates was attributed to sampling point and microbial group but could be moderated by publication year, country cluster, and methods for microbial confirmation. The pooled prevalence estimates increased over time as more studies became available, whereby the odds were higher in studies from 2010 onwards than studies before 2010. From the analyses, S. aureus presented the greatest safety concern in African IFM. Future microbiological studies should take into consideration different IFM sampling points and advanced analytical methods to identify pathogens.

This study was conducted to estimate the hemolytic uremic syndrome (HUS) risk associated with consumption of producer-distributor bulk milk (PDBM) contaminated with Shiga toxin–producing Escherichia coli (STEC) in South Africa. Data were obtained from recently completed studies in South Africa taking into account prior collected prevalence data of STEC in raw and pasteurized PDBM and survey information from producer-distributor outlets and households. Inputs for the models were complemented with data from published and unpublished literature. A probabilistic exposure model was developed with Monte Carlo simulation in Excel add-in software using @Risk software. Hazard characterization was based on an exponential dose-response model to calculate the probability of illness from STEC infection in individuals 5 years and younger and individuals older than 5 years. The estimated mean STEC level was 0.12 CFU/mL (95% confidence interval [CI]: 0 to 1.2; r ¼ 0.34) for raw PDBM and 0.08 CFU/mL (95% CI: 0 to 1; r ¼ 0.27) for pasteurized PDBM. A higher risk of HUS cases per year was recorded in raw than in pasteurized PDBM and also in individuals younger than 5 years of age. For every 100,000 servings consumed, the expected median numbers of HUS cases per year from raw PDBM were 52 for 5 years and younger and 3.2 for older than 5 years. The median numbers of cases per year for pasteurized PDBM were 47 for 5 years and younger and 2.9 for older than 5 years. Sensitivity analysis revealed that serving volume and time taken to sell PDBM at producer-distributor outlets were the factors with the greatest impact on probability of illness. The models developed in this study are an example of risk assessments for milk produced and marketed from similar scenarios across the globe.

We present here draft genome sequences of Enterococcus mundtii strains K7-EM, P2-EM, C11-EM, and H18-EM, which were isolated from slaughterhouse equipment, carcasses, and personnel of small- and medium-sized beef slaughterhouses in Kenya.

Avian influenza virus (H5N1) is a rapidly disseminating infection that affects poultry and, potentially, humans. Because the avian virus has already adapted to several mammalian species, decreasing the rate of avian-mammalian contacts is critical to diminish the chances of a total adaptation of H5N1 to humans. To prevent the pandemic such adaptation could facilitate, a biology-specific disease surveillance model is needed, which should also consider geographical and socio-cultural factors. Here, we conceptualized a surveillance model meant to capture H5N1-related biological and cultural aspects, which included food processing, trade and cooking-related practices, as well as incentives (or disincentives) for desirable behaviours. This proof of concept was tested with data collected from 378 Egyptian and Nigerian sites (local [backyard] producers/live bird markets/village abattoirs/commercial abattoirs and veterinary agencies). Findings revealed numerous opportunities for pathogens to disseminate, as well as lack of incentives to adopt preventive measures, and factors that promoted epidemic dissemination. Supporting such observations, the estimated risk for H5N1-related human mortality was higher than previously reported. The need for multidimensional disease surveillance models, which may detect risks at higher levels than models that only measure one factor or outcome, was supported. To develop efficient surveillance systems, interactions should be captured, which include but exceed biological factors. This low-cost and easily implementable model, if conducted over time, may identify focal instances where tailored policies may diminish both endemicity and the total adaptation of H5N1 to the human species.

Limited knowledge is currently available on the influence of fish thawing and subsequent storage conditions on bacterial growth kinetics, succession, and diversity alongside the production of biogenic amines. This study aimed to address these factors during the thawing and subsequent storage of mackerel. Thawing was either done fast in 18°C water for 2 h or slowly at 30°C overnight. Subsequent storage was at 30°C (ambient) for 36 h and 2 to 5°C (refrigerated) for 12 days. The cultivation methods used were total viable counts, hydrogen sulfide-producing bacteria, and Pseudomonas . Maximum growth rate, population density, and lag time were fitted on the counts using the Baranyi model. The bacterial diversity and succession were based on sequencing of 16S rRNA amplicons, and biogenic amines were quantified on high-pressure liquid chromatography-UV. The results show that lag time of hydrogen sulfide-producing bacteria was significantly affected by both thawing methods, and further, the interaction between thawing and storage significantly affected the maximum growth rate of these bacteria. However, the maximum growth rate of Pseudomonas was higher during refrigerated storage compared with storage at ambient temperature. Total viable counts showed longer lag time and reduced growth rate under refrigerated storage. Higher bacterial diversity was correlated to slow thawing and storage at ambient temperature compared with slow thawing and refrigerated storage. Overall, Acinetobacter and Psychrobacter genera were the dominant bacterial populations. The amine levels were low and could not be differentiated along the thawing and storage approaches, despite a clear increase in bacterial load, succession, and diversity. This corresponded well with the low abundance of biogenic amine-producing bacteria, with the exception of the genus Proteus , which was 8.6% in fast-thawed mackerel during storage at ambient temperature. This suggests that the decarboxylation potential is dependent on both microbial load and microbial community structure.

Current approaches such as inspections, audits, and end product testing cannot detect the distribution and dynamics of microbial contamination. Despite the implementation of current food safety management systems, foodborne outbreaks linked to fresh produce continue to be reported. A microbial assessment scheme and statistical modeling were used to systematically assess the microbial performance of core control and assurance activities in five Kenyan fresh produce processing and export companies. Generalized linear mixed models and correlated random-effects joint models for multivariate clustered data followed by empirical Bayes estimates enabled the analysis of the probability of contamination across critical sampling locations (CSLs) and factories as a random effect. Salmonella spp. and Listeria monocytogenes were not detected in the final products. However, none of the processors attained the maximum safety level for environmental samples. Escherichia coli was detected in five of the six CSLs, including the final product. Among the processing-environment samples, the hand or glove swabs of personnel revealed a higher level of predicted contamination with E. coli , and 80% of the factories were E. coli positive at this CSL. End products showed higher predicted probabilities of having the lowest level of food safety compared with raw materials. The final products were E. coli positive despite the raw materials being E. coli negative for 60% of the processors. There was a higher probability of contamination with coliforms in water at the inlet than in the final rinse water. Four (80%) of the five assessed processors had poor to unacceptable counts of Enterobacteriaceae on processing surfaces. Personnel-, equipment-, and product-related hygiene measures to improve the performance of preventive and intervention measures are recommended.

Epidemiological studies show a definite connection between areas of high aflatoxin content and a high occurrence of human hepatocellular carcinoma (HCC). Hepatitis B virus in individuals further increases the risk of HCC. The two risk factors are prevalent in rural Kenya and continuously predispose the rural populations to HCC. A quantitative cancer risk assessment therefore quantified the levels at which potential pre- and postharvest interventions reduce the HCC risk attributable to consumption of contaminated maize and groundnuts. The assessment applied a probabilistic model to derive probability distributions of HCC cases and percentage reductions levels of the risk from secondary data. Contaminated maize and groundnuts contributed to 1,847 ± 514 and 158 ± 52 HCC cases per annum, respectively. The total contribution of both foods to the risk was additive as it resulted in 2,000 ± 518 cases per annum. Consumption and contamination levels contributed significantly to the risk whereby lower age groups were most affected. Nonetheless, pre- and postharvest interventions might reduce the risk by 23.0-83.4% and 4.8-95.1%, respectively. Therefore, chronic exposure to aflatoxins increases the HCC risk in rural Kenya, but a significant reduction of the risk can be achieved by applying specific pre- and postharvest interventions.

The ever decreasing cost and increase in throughput of next generation sequencing (NGS) techniques have resulted in a rapid increase in availability of NGS data. Such data have the potential for rapid, reproducible and highly discriminative characterization of pathogens. This provides an opportunity in microbial risk assessment to account for variations in survivability and virulence among strains. A major challenge towards such attempts remains the highly dimensional nature of genomic data versus the number of isolates. Machine learning-based (ML) predictive risk modelling provides a solution to this “curse of dimensionality” while accounting for individual effects that are dependent on interactions with other genetic and environmental factors. This pilot study explores the potential of ML in the prediction of health endpoints resulting from shigatoxigenic E. coli (STEC) infection. Accessory genes in amino acid sequences were used as model input to predict and differentiate health outcomes in STEC infections including diarrhea, bloody diarrhea, hemolytic uremic syndrome and their combinations. Outcomes severity was also distinguished by hospitalization. A matrix of percent similarity between accessory genes and the E. coli genomes was generated and subsequently used as input for ML. The performances of ML algorithms random forest, support vector machine (radial and linear kernel), gradient boosting, and logit boost were compared. Logit boost was the best model showing an outcome prediction accuracy of 0.75 (95% CI: 0.60, 0.86), an excellent or substantial performance (Kappa = 0.72). Important genetic predictors of riskier STEC clinical outcomes included proteins involved in initial attachment to the host cell, persistence of plasmids or genomic islands, conjugative plasmid transfer and formation of sex pili, regulation of locus of enterocyte effacement expression, post-translational acetylation of proteins, facilitation of the rearrangement or deletion of sections within the pathogenic islands and transport macromolecules across the cell envelope. We propose further studies are proposed on the proteins with undefined or unclear functionality. One protein family in particular predicted HUS outcome. Toxin-antitoxin systems are potential stress adaptation markers which may mediate environmental persistence of strains in diverse sources. We foresee the application of ML approach to the set-up of real-time online analysis of whole genome sequence data to estimate the human health risk at the population or strain level. The ML approach is envisaged to support the prediction of more specific STEC clinical endpoints type by inputting isolate sequence data.