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SURVIVAL OF LISTERIA MONOCYTOGENES AND ESCHERICHIA COLI O157:H7 DURING AGING OF GOUDA CHEESE MADE USING UNPASTEURIZED MILK
Title
SURVIVAL OF LISTERIA MONOCYTOGENES AND ESCHERICHIA COLI O157:H7 DURING AGING OF GOUDA CHEESE MADE USING UNPASTEURIZED MILK
Creator
Natarajan, Vidya
Date
2018, 2018-05
Description
The FDA code of federal regulations states that cheeses made using unpasteurized milk must be aged for a period of at least 60 days to...
Show moreThe FDA code of federal regulations states that cheeses made using unpasteurized milk must be aged for a period of at least 60 days to minimize the inherent risks associated with unpasteurized milk. However, there have been several foodborne outbreaks associated with 60-day aged semi-soft cheeses made using unpasteurized milk, specifically Gouda cheese. In this study, Gouda cheese was manufactured using unpasteurized milk artificially-inoculated with Listeria monocytogenes (1 or 3 log CFU/mL) and Escherichia coli O157:H7 (1 log CFU/mL). The Gouda cheese was pressed, brined, waxed, and aged at 10°C for 90 (for the 1 log CFU/mL) or 150 (for the 3 log CFU/mL) days. Samples were assessed during cheese manufacture and aging for survival of the pathogen as well as for the population dynamics of the native microflora including Enterobacteriaceae, yeast and mold, lactic acid bacteria, and mesophilic bacteria. In addition, cheese samples during aging were also analyzed for property characteristics including salt and moisture content, fat in solid content, pH, and water activity. Results determined that the population levels of both pathogens significantly increased during manufacture. During aging of the Gouda cheese, E. coli O157:H7 was capable of survival only until 49 days and was henceforth not detected via enrichment. For L. monocytogenes, pathogen populations were 2.07±0.12 log and 1.26±0.00 log CFU/g at 60 and 90 days of aging, respectively, for the 1 log CFU/mL initial inoculation level. Compared to day 60 (2.31±0.92 log CFU/g) of aging, the population of L. monocytogenes for the Gouda cheese made with the 3 log CFU/mL initial inoculation level was significantly higher (p<0.05) on both 90 and 150 d of aging (4.62±0.25 and 6.00±0.72 log CFU/g, respectively). During aging, the populations of lactic acid and mesophilic bacterial were significantly higher than other microflora categories. The population of yeast and mold displayed an increasing trend in population, whereas Enterobacteriaceae populations were highly unsteady. Increases in lactic acid bacterial populations were accompanied by decreases in pH and pathogen populations. These results indicate that the characteristics of Gouda cheese and the native microflora population may play a pivotal role in survival and growth of pathogens. Overall, this study suggests that the current 60-day aging regulation, while sufficient to control E. coli O157:H7, may not be suitable to control the risk of L. monocytogenes in Gouda cheese.The population of yeast and mold displayed an increasing trend in population, whereas Enterobacteriaceae populations were highly unsteady. Increases in lactic acid bacterial populations were accompanied by decreases in pH and pathogen populations. These results indicate that the characteristics of Gouda cheese and the native microflora population may play a pivotal role in survival and growth of pathogens. Overall, this study suggests that the current 60-day aging regulation, while sufficient to control E. coli O157:H7, may not be suitable to control the risk of L. monocytogenes in Gouda cheese.
M.S.in Food Safety and Technology, May 2018
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ENLARGED PERIVASCULAR SPACES IN COMMUNITY-BASED OLDER ADULTS
Title
ENLARGED PERIVASCULAR SPACES IN COMMUNITY-BASED OLDER ADULTS
Creator
Javierre Petit, Carles
Date
2020
Description
Enlarged perivascular spaces (EPVS) have been associated with aging, increased stroke risk, decreased cognitive function and vascular dementia...
Show moreEnlarged perivascular spaces (EPVS) have been associated with aging, increased stroke risk, decreased cognitive function and vascular dementia. Furthermore, recent studies have investigated the links of EPVS with the glymphatic system (GS), since perivascular spaces are thought to play a major role as the main channels for clearance of interstitial solutes from the brain. However, the relationship of EPVS with age-related neuropathologies is not well understood. Therefore, more conclusive studies are needed to elucidate specific relationships between EPVS and neuropathologies. After demonstration of their neuropathologic correlates, detailed assessment of EPVS severity could provide as a potential biomarker for specific neuropathologies.In this dissertation, our focus was twofold: to develop a fully automatic EPVS segmentation model via deep learning with a set of guidelines for model optimization, and to evaluate both manual and automatic assessment of EPVS severity to investigate the neuropathologic correlates of EPVS, and their contribution to cognitive decline, by combining ex-vivo brain magnetic resonance imaging (MRI) and pathology (from autopsy) in a large community-based cohort of older adults. This project was structured as follows. First, a manual approach was used to assess neuropathologic and cognitive correlates of EPVS burden in a large dataset of community-dwelling older adults. MR images from each participant were rated using a semiquantitative 4-level rating scale, and a group of identified EPVS was histologically evaluated. Two groups of participants in descending order of average cognitive impairment were defined based and studied. Elasticnet regularized ordinal logistic regression was used to assess the neuropathologic correlates of EPVS burden in each group, and linear mixed effects models were used to investigate the associations of EPVS burden with cognitive decline. Second, a fully automatic EPVS segmentation model was implemented via deep learning (DL) using a small dataset of 10 manually segmented brain MR images. Multiple techniques were evaluated to optimize performance, mainly by implementing strategies to reduce model overfitting. The final segmentation model was evaluated in an independent test set and the performance was validated with an expert radiologist. Third, the DL segmentation model was used to segment and quantify EPVS. Quantified EPVS (qEPVS) were evaluated by combining ex-vivo MRI, pathology, and longitudinal cognitive evaluation. EPVS quantification allowed to study qEPVS both in the whole brain and regionally. Two different qEPVS metrics were studied. Elastic-net regularized linear regression was used to assess the neuropathologic correlates of qEPVS within each region of interest (ROI) under study, and linear mixed effects models were used to investigate the associations of qEPVS with cognitive decline. Finally, a preliminary study investigated the longitudinal associations of qEPVS with time. The DL segmentation model was re-trained using 4 in-vivo MR images. EPVS were segmented and quantified in a large longitudinal cohort where each participant was imaged at multiple timepoints. Factors that influenced segmentation performance across timepoints were evaluated, and linear mixed effects models controlling for these factors were used to investigate the associations of qEPVS with time.
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