Rapid detection of foodborne pathogen is one of the most urgent problems in the world, because foodborne pathogen could cause serious illness, such as nausea, vomiting and diarrhea. We have developed a sensitive microfluidic system based on dendrimers and aptamers for rapid detection of Escherichia coli O157:H7 at very low cells concentration. Dendrimers, with high level of functional groups and homogeneous spherical shape, are prefect nanoscale polymers used as a template material by increasing sensitivity and specificity of analytes detection in microfluidics. In this work, we develop a sensitive microfluidic system based on dendrimers and aptamers for detecting Escherichia coli O157:H7 at very low cell concentrations. Carboxyl functionalized G7-polyamidoamine (PAMAM-COOH) dendrimers are immobilized on (3-aminopropyl)-trimethoxysilane (APTMS) pretreated microfluidic channels. The aptamers are subsequently conjugated on the immobilized dendrimes through chemicals. The sensitivity and specificity are validated by injecting fluorescein isothiocyanate (FITC) labelled Escherichia coli O157:H7 at various cells concentration into the resulting microchannels, indicating that the detectable cells concentration can be reached as low as 100 (cells/ml) and the detection time is 10 hours. To further exploit and improve the work efficiency our microfluidic device, the microfluidic channel is designed into a staggered herringbone microchannel (SHM) to create the chaotic dynamics inside the microfluidic device, and the SHM is then simulated by a COMSOL software showing that the staggered herringbone structures can improve chaotic dynamics of designed microchannel and will enhance the probability of particles to attach on the surface of microdevice. All the results show that our approach has the potential to develop the field of rapid and accurate detection on foodborne pathogens.

Cronobacter sakazakii is a gram-negative opportunistic pathogen known to survive in dry environments and food matrices, such as infant formula. This foodborne bacterium can cause fatal human infections of the blood, central nervous system, and gastrointestinal tract; it is also problematic in wounds and urinary tract infections. Preterm infants and immunocompromised individuals are in higher risk categories related to necrotizing enterocolitis, neonatal sepsis, and meningitis due to this organism. Therefore, there is a need for increased understanding of how this bacterium is able to persist in thermally treated low-moisture products that do not support growth. The objective of this research is to identify genes and mechanisms in C. sakazakii that contribute to its resistance to desiccation and survival in low-moisture food matrices, including powdered infant formula. C. sakazakii sequence type 4 (ST4) is of particular interest as it is often the cause of neonatal infections originating from contaminated feedings of powder infant formula. The method chosen to explore these genetic patterns is massively parallel transposon insertion sequencing (Tn-seq). The E. coli strain MFDpir was used to facilitate transposon insertional mutagenesis to create a library of mutated C. sakazakii. Three different C. sakazakii ST4 isolates of different origins (clinical, environmental, and infant formula-derived) were selected for this study. Once transposon mutagenesis occurred with the aid of E. coli MFDpir, the three mutant libraries were subjected to desiccation stress in a closed system equilibrated to 11.3% relative humidity. The surviving mutant genomes were analyzed with Tn-seq. The sequencing data revealed that, while transposition events did occur successfully within the genomes of each of the selected C. sakazakii isolates, these events were not dense enough to draw biological conclusions nor statistical inferences concerning which genes contribute to this organism’s uncanny desiccation tolerance. However, we concluded that the Tn-seq method is a promising tool with this organism of interest, despite incomplete results in this first round of experimentation.

<p dir="ltr">The increasing consumption of fresh produce such as cantaloupes, watermelons, lettuce, and cucumbers has been linked to multiple foodborne outbreaks, highlighting the urgency of implementing effective measures to prevent bacterial contamination, colonization, and internalization. This study evaluates various antimicrobial chemical washing solutions to reduce foodborne pathogens and improve the microbial quality of fresh produce. The research investigates the impact of netting density on cantaloupe rind surfaces and assesses the efficacy of sodium hypochlorite (FAC), peracetic acid (PAA), and chlorine dioxide (ClO₂) against <i>Escherichia coli</i>, <i>Listeria monocytogenes</i>, and <i>Salmonella </i>Typhimurium. Results suggest that higher netting densities decreased the efficacy of these treatments, with smooth rind cantaloupes showing the highest bacterial reduction when treated with PAA and FAC. Further investigations into the inline application of antimicrobial washing solutions under commercial packing house conditions revealed that combining ClO₂ and PAA significantly reduced pathogen loads on cantaloupes and watermelons without adversely affecting their sensory qualities. Additionally, the study assessed the effectiveness of PAA, FAC, and accelerated hydrogen peroxide (AHP) on fresh cucumbers, broccoli, and lettuce under conditions that emulate commercial retail facilities. The treatments achieved significant log reductions in aerobic mesophilic bacteria and common pathogens, highlighting the critical role of these solutions in preventing cross-contamination during postharvest handling. Finally, the research also examined the internalization of foodborne pathogens in lettuce and cucumber plants, revealing high recovery rates of <i>E.</i><i> </i><i>coli </i>O157:O157H7 and <i>S.</i><i> </i>Typhimurium from contaminated seeds, soil, leaves, and blossoms. These findings highlight the potential for pathogen colonization and persistence in fresh produce, indicating the need for preventative agricultural practices and microbial control measures throughout the cultivation and handling processes. Together, these studies suggest that integrating effective antimicrobial washing solutions with proper storage conditions and good agricultural practices is essential for enhancing fresh produce's microbial safety and shelf-life, thereby reducing the risk of foodborne illnesses.</p>

Salmonellosis, a significant foodborne illness in humans, is caused by Salmonella, with poultry and poultry products acting as significant reservoirs and sources of human infection. Salmonella enterica subspecies enterica serotype Reading has recently emerged as a notable foodborne pathogen responsible for extensive multistate human outbreaks in North America. This study focused on evaluating the capacity of the emerged serotype to colonize broiler breeder reproductive tissues and potentially contaminate eggs, indicating the potential for vertical transmission. For this investigation, two Salmonella Reading strains were utilized, one associated with outbreaks and another non-outbreak strain. Both strains were initially modified with bioluminescent marker genes to facilitate tracking post-experimental infection in broiler breeders. The results indicated that both strains could colonize the reproductive tract of infected hens and be transmitted vertically through the eggs. This finding enhances our understanding of the colonization and vertical transmission capabilities of this serotype in broiler breeders.

No description available.

This thesis is concerned with the application of recently developed epidemiological and statistical tools to inform the optimisation of a national surveillance strategy of considerable importance to human health. The results of a series of epidemiological investigations of surveillance strategies for zoonotic Salmonella are presented. Salmonella are one of the most common and serious zoonotic foodborne pathogenic bacteria globally. These studies were motivated by the increasing focus on the cost-effectiveness of surveillance while maintaining consumer confidence in food supply. Although data from the Danish Salmonella surveillance and control programme has been used in these investigations, the techniques may be readily applied to other surveillance data of similar quality. The first study describes the spatial epidemiological features of Danish Salmonella surveillance and control programme data from 1995 to 2004, using a novel method of spatially adaptive smoothing. The conditional probability of a farm being a case was consistently high in the the south-west of Sonderjylland on the Jutland peninsula, identifying this area for further investigation and targeted surveillance. The identification of clustering of case farms led into the next study, which closely examines one year of data, 2003, for patterns of spatial dependency. K-function analyses provided evidence for aggregation of Salmonella case farms over that of all farms at distances of up to six kilometres. Visual semivariogram analyses of random farm-level effects from a Bayesian logistic regression model (adjusted for herd size) of Salmonella seropositivity, revealed spatial dependency between pairs of farms up to a distance of four kilometres apart. The strength of the spatial dependency was positively associated with slaughter pig farm density. We describe how this might inform the surveillance programme by potentially targeting herds within a four kilometre radius of those with high levels of Salmonella infection. In the third study, farm location details, routinely recorded surveillance information, and industry survey data from 1995 were combined to build a logistic seroprevalence model. This identified wet-feeding and specific pathogen free herd health status as protective factors for Salmonella seropositivity, while purchasing feed was a risk factor. Once adjusting for these covariates, we identified pockets of unexplained risk for Salmonella seropositivity and found spatial dependency at distances of up to six km (95% CI: 2–35 km) between farms. A generalised linear spatial model was fitted to the Jutland data allowing formal estimation of the range of spatial correlation and a measure of the uncertainty about it. There was a large within-farm component to the variance, suggesting that gathering more farm level information would be advantageous if this approach was to be used to target surveillance strategy. The fourth study again considers data from the whole study period, 1995 to 2004. A detailed temporal analysis of the data revealed there was no consistent seasonal pattern and correspondingly no benefit in targeting sampling to particular times of the year. Spatiotemporal analyses suggested a local epidemic of increased seroprevalence occured in west Jutland in late 2000. Lorelogram analyses showed a defined period of statistically significant temporal dependency, suggesting that there is little value in sampling more frequently than every 10 weeks on the average farm. The final study uses findings from the preceding chapters to develop a zero-inflated binomial model which predicts which farms are most at risk of Salmonella, and then preferentially samples these high-risk farms. This type of modelling allows assessment of similarities and differences between factors that affect herd infection status (introduction) and those that affect the seroprevalence in infected herds (persistence and spread). The model suggested that many of the herds where Salmonella was not detected were infected but at a low prevalence. Using cost and sensitivity, we compared the results with those under the standard sampling scheme based on herd size, and the recently introduced risk-based approach. Model based results were less sensitive, but showed significant cost savings. Further model refinements, sampling schemes, and the methods to evaluate their performance are important areas for future work, and should continue to occur in direct consultation with Danish authorities.

This thesis is concerned with the application of recently developed epidemiological and statistical tools to inform the optimisation of a national surveillance strategy of considerable importance to human health. The results of a series of epidemiological investigations of surveillance strategies for zoonotic Salmonella are presented. Salmonella are one of the most common and serious zoonotic foodborne pathogenic bacteria globally. These studies were motivated by the increasing focus on the cost-effectiveness of surveillance while maintaining consumer confidence in food supply. Although data from the Danish Salmonella surveillance and control programme has been used in these investigations, the techniques may be readily applied to other surveillance data of similar quality. The first study describes the spatial epidemiological features of Danish Salmonella surveillance and control programme data from 1995 to 2004, using a novel method of spatially adaptive smoothing. The conditional probability of a farm being a case was consistently high in the the south-west of Sonderjylland on the Jutland peninsula, identifying this area for further investigation and targeted surveillance. The identification of clustering of case farms led into the next study, which closely examines one year of data, 2003, for patterns of spatial dependency. K-function analyses provided evidence for aggregation of Salmonella case farms over that of all farms at distances of up to six kilometres. Visual semivariogram analyses of random farm-level effects from a Bayesian logistic regression model (adjusted for herd size) of Salmonella seropositivity, revealed spatial dependency between pairs of farms up to a distance of four kilometres apart. The strength of the spatial dependency was positively associated with slaughter pig farm density. We describe how this might inform the surveillance programme by potentially targeting herds within a four kilometre radius of those with high levels of Salmonella infection. In the third study, farm location details, routinely recorded surveillance information, and industry survey data from 1995 were combined to build a logistic seroprevalence model. This identified wet-feeding and specific pathogen free herd health status as protective factors for Salmonella seropositivity, while purchasing feed was a risk factor. Once adjusting for these covariates, we identified pockets of unexplained risk for Salmonella seropositivity and found spatial dependency at distances of up to six km (95% CI: 2–35 km) between farms. A generalised linear spatial model was fitted to the Jutland data allowing formal estimation of the range of spatial correlation and a measure of the uncertainty about it. There was a large within-farm component to the variance, suggesting that gathering more farm level information would be advantageous if this approach was to be used to target surveillance strategy. The fourth study again considers data from the whole study period, 1995 to 2004. A detailed temporal analysis of the data revealed there was no consistent seasonal pattern and correspondingly no benefit in targeting sampling to particular times of the year. Spatiotemporal analyses suggested a local epidemic of increased seroprevalence occured in west Jutland in late 2000. Lorelogram analyses showed a defined period of statistically significant temporal dependency, suggesting that there is little value in sampling more frequently than every 10 weeks on the average farm. The final study uses findings from the preceding chapters to develop a zero-inflated binomial model which predicts which farms are most at risk of Salmonella, and then preferentially samples these high-risk farms. This type of modelling allows assessment of similarities and differences between factors that affect herd infection status (introduction) and those that affect the seroprevalence in infected herds (persistence and spread). The model suggested that many of the herds where Salmonella was not detected were infected but at a low prevalence. Using cost and sensitivity, we compared the results with those under the standard sampling scheme based on herd size, and the recently introduced risk-based approach. Model based results were less sensitive, but showed significant cost savings. Further model refinements, sampling schemes, and the methods to evaluate their performance are important areas for future work, and should continue to occur in direct consultation with Danish authorities.

This thesis is concerned with the application of recently developed epidemiological and statistical tools to inform the optimisation of a national surveillance strategy of considerable importance to human health. The results of a series of epidemiological investigations of surveillance strategies for zoonotic Salmonella are presented. Salmonella are one of the most common and serious zoonotic foodborne pathogenic bacteria globally. These studies were motivated by the increasing focus on the cost-effectiveness of surveillance while maintaining consumer confidence in food supply. Although data from the Danish Salmonella surveillance and control programme has been used in these investigations, the techniques may be readily applied to other surveillance data of similar quality. The first study describes the spatial epidemiological features of Danish Salmonella surveillance and control programme data from 1995 to 2004, using a novel method of spatially adaptive smoothing. The conditional probability of a farm being a case was consistently high in the the south-west of Sonderjylland on the Jutland peninsula, identifying this area for further investigation and targeted surveillance. The identification of clustering of case farms led into the next study, which closely examines one year of data, 2003, for patterns of spatial dependency. K-function analyses provided evidence for aggregation of Salmonella case farms over that of all farms at distances of up to six kilometres. Visual semivariogram analyses of random farm-level effects from a Bayesian logistic regression model (adjusted for herd size) of Salmonella seropositivity, revealed spatial dependency between pairs of farms up to a distance of four kilometres apart. The strength of the spatial dependency was positively associated with slaughter pig farm density. We describe how this might inform the surveillance programme by potentially targeting herds within a four kilometre radius of those with high levels of Salmonella infection. In the third study, farm location details, routinely recorded surveillance information, and industry survey data from 1995 were combined to build a logistic seroprevalence model. This identified wet-feeding and specific pathogen free herd health status as protective factors for Salmonella seropositivity, while purchasing feed was a risk factor. Once adjusting for these covariates, we identified pockets of unexplained risk for Salmonella seropositivity and found spatial dependency at distances of up to six km (95% CI: 2–35 km) between farms. A generalised linear spatial model was fitted to the Jutland data allowing formal estimation of the range of spatial correlation and a measure of the uncertainty about it. There was a large within-farm component to the variance, suggesting that gathering more farm level information would be advantageous if this approach was to be used to target surveillance strategy. The fourth study again considers data from the whole study period, 1995 to 2004. A detailed temporal analysis of the data revealed there was no consistent seasonal pattern and correspondingly no benefit in targeting sampling to particular times of the year. Spatiotemporal analyses suggested a local epidemic of increased seroprevalence occured in west Jutland in late 2000. Lorelogram analyses showed a defined period of statistically significant temporal dependency, suggesting that there is little value in sampling more frequently than every 10 weeks on the average farm. The final study uses findings from the preceding chapters to develop a zero-inflated binomial model which predicts which farms are most at risk of Salmonella, and then preferentially samples these high-risk farms. This type of modelling allows assessment of similarities and differences between factors that affect herd infection status (introduction) and those that affect the seroprevalence in infected herds (persistence and spread). The model suggested that many of the herds where Salmonella was not detected were infected but at a low prevalence. Using cost and sensitivity, we compared the results with those under the standard sampling scheme based on herd size, and the recently introduced risk-based approach. Model based results were less sensitive, but showed significant cost savings. Further model refinements, sampling schemes, and the methods to evaluate their performance are important areas for future work, and should continue to occur in direct consultation with Danish authorities.