Introduction: The purpose of the study was to determine and model the growth rates of L. monocytogenes in cooked cured ham stored at various temperatures. Material and Methods: Samples of cured ham were artificially contaminated with a mixture of three L. monocytogenes strains and stored at 3, 6, 9, 12, or 15°C for 16 days. The number of listeriae was determined after 0, 1, 2, 3, 5, 7, 9, 12, 14, and 16 days. A series of decimal dilutions were prepared from each sample and plated onto ALOA agar, after which the plates were incubated at 37°C for 48 h under aerobic conditions. The bacterial counts were logarithmised and analysed statistically. Five repetitions of the experiment were performed. Results: Both storage temperature and time were found to significantly influence the growth rate of listeriae (P < 0.01). The test bacteria growth curves were fitted to three primary models: the Gompertz, Baranyi, and logistic. The mean square error (MSE) and Akaike’s information criterion (AIC) were calculated to evaluate the goodness of fit. It transpired that the logistic model fit the experimental data best. The natural logarithms of L. monocytogenes’ mean growth rates from this model were fitted to two secondary models: the square root and polynomial. Conclusion: Modelling in both secondary types can predict the growth rates of L. monocytogenes in cooked cured ham stored at each studied temperature, but mathematical validation showed the polynomial model to be more accurate.

OBJECTIVE To determine whether dual-energy CT (DECT) could accurately differentiate the composition of common canine uroliths in a phantom model. SAMPLE 30 canine uroliths with pure compositions. PROCEDURES Each urolith was composed of ≥ 70% struvite (n = 10), urate (8), cystine (5), calcium oxalate (4), or brushite (3) as determined by standard laboratory methods performed at the Canadian Veterinary Urolith Centre. Uroliths were suspended in an agar phantom, and DECT was performed at low (80 kV) and high (140 kV) energies. The ability of low- and high-energy CT numbers, DECT number, and DECT ratio to distinguish uroliths on the basis of composition was assessed with multivariate ANOVA. RESULTS No single DECT measure differentiated all urolith types. The DECT ratio differentiated urate uroliths from all other types of uroliths. The DECT and low-energy CT numbers were able to differentiate between 8 and 7 pairs of urolith types, respectively. CONCLUSIONS AND CLINICAL RELEVANCE Results indicated that DECT was unable to differentiate common types of canine uroliths in an in vitro model; therefore, it is unlikely to be clinically useful for determining urolith composition in vivo. Given that the primary reasons for determining urolith composition in vivo are to predict response to shock wave lithotripsy and develop a treatment plan, future research should focus on the correlation between DECT measurements and urolith fragility rather than urolith composition.