Cross-contamination in the kitchen: A model for quantitative microbiological risk assessment.

A quantitative microbiological risk assessment model for the cross-contamination transmission route in the kitchen (KCC) is presented. Bacteria are transmitted from contaminated (chicken) meat to hands, kitchen utensils, and other surfaces, subsequently contaminating a salad. The model aims to estimate the fraction of bacteria on the meat that is ingested due to cross-contamination, determine the importance of the different transmission routes, and assess the effect of scenarios (interventions) on the fraction ingested. The cross-contamination routes defined, bacterial source-to-recipient transfer fractions as available and derived from literature, and important characteristics (e.g., washing in cold water vs. hot water with soap) shaped the KCC model. With this model, 32 scenarios of an eight-step preparation of a "meat and salad" meal in a domestic kitchen were stochastically simulated. The "cutting board-salad" route proved dominant and the salad plays a major role in the final exposure. A realistic scenario (washing hands, cutting board, and knife with cold water after cutting the meat) estimates that a mean fraction of 3.2E - 3 of the bacteria on the meat is ingested. In the case of "hand washing with hot water and soap" and "cutting board and knife replacement," the mean fraction ingested is 3.6E - 6. For a subsequent meal, where the contaminated sources were kitchen fomites, the estimated mean fraction is 4.3E - 4. In case of hamburger, part of the bacteria is unavailable for cross-contamination, resulting in a mean fraction ingested of about 5.4E - 5. The role of the dishcloth in cross-contamination transmission proved to be minor.

Terahertz time-domain spectroscopy for powder compact porosity and pore shape measurements: An error analysis of the anisotropic bruggeman model.

Terahertz time-domain spectroscopy (THz-TDS) is a novel technique which has been applied for pore structure analysis and porosity measurements. For this, mainly the anisotropic Bruggeman (AB-EMA) model is applied to correlate the effective refractive index (n eff) of a tablet and the porosity as well as to evaluate the pore shape based on the depolarisation factor L. This paper investigates possible error sources of the AB-EMA for THz-TDS based tablet analysis. The effect of absorption and tablet anisotropy - changes of pore shape with porosity and density distribution - have been investigated. The results suggest that high tablet absorption has a negligible effect on the accuracy of the AB-EMA. In regards of tablet anisotropy the accuracy of the porosity determination is not impaired significantly. However, density distribution and variations in the pore shape with porosity resulted in an unreliable extraction of the tablet pore shape. As an extension of the AB-EMA a new concept was introduced to convert the model into bounds for L. This new approach was found useful to investigate tablet pore shape but also the applicability of the AB-EMA for an unknown set of data.

Elevated plasma fractalkine levels are associated with higher levels of IL-6, Apo-B, LDL-C and insulin, but not with body composition in a large female twin sample.

OBJECTIVE: Plasma fractalkine (FRACT) is involved in the development of numerous inflammatory conditions including atherosclerosis. It is associated with type 2 diabetes mellitus and adipose inflammation. However, whether FRACT is associated with major risk factors for cardiovascular disease, in particular obesity, metabolic syndrome and blood lipids, is virtually unknown. METHODS: The study included a large community-based sample of 3306 middle-aged women drawn from the general UK population. Blood samples were analyzed for circulating levels of FRACT, leptin, insulin, glucose, LDL-C, HDL-C, Apo-A, ApoB and IL-6. Obesity was assessed by fat body mass (FBM) using dual-energy x-ray absorptiometry and by body mass index (BMI). RESULTS: We found no association between FRACT and body composition, in particular adiposity. Obese and non obese subjects with metabolic syndrome tended to have higher levels of FRACT compared with non-obese subjects without metabolic syndrome but this did not reach statistical significance. Most importantly we report significant correlations between FRACT and circulating IL-6, Apo-B, LDL-C and insulin. The associations with IL-6 and Apo-B were particularly significant (P-value<0.001), and survived correction for multiple testing and adjustment for age and other covariates. CONCLUSION: Higher FRACT levels correlated with elevated levels of IL-6, Apo-B, LDL-C and insulin, all known risk factors for several clinical related diseases suggesting a potential role of FRACT in inflammation and tissue injury. Variations of FRACT levels are not influenced by body composition and are not correlated with leptin indicating that fat mass alone is not responsible for elevation of FRACT seen in obese individuals.