Impact of Phyllantus niruri and Lactobacillus amylovorus SGL 14 in a mouse model of dietary hyperoxaluria.

Hyperoxaluria is a pathological condition which affects long-term health of kidneys. The present study evaluates the impact of the combination of Lactobacillus amylovorus SGL 14 and the plant extract Phyllantus niruri (namely Phyllantin 14™) on dietary hyperoxaluria. Safety and efficacy of Phyllantin 14 have been evaluated in vivo. Mice C57BL6 fed a high-oxalate diet were compared to mice fed the same diet administered with Phyllantin 14 by gavage for 6 weeks. Control mice were fed a standard diet without oxalate. No adverse effects were associated to Phyllantin 14 supplementation, supporting its safety. Mice fed a high-oxalate diet developed significant hyperoxaluria and those administered with Phyllantin 14 showed a reduced level of urinary oxalate and a lower oxalate-to-creatinine ratio. Soluble and insoluble caecal oxalate were significantly lower in treated group, a finding in agreement with the colonisation study, i.e. mice were colonised with SGL 14 after 3 weeks. Microbiota analysis demonstrated that both oxalate diet and Phyllantin 14 can differently modulate the microbiota. In conclusion, our findings suggest that Phyllantin 14 supplementation represents a potential supportive approach for reducing urinary oxalate and/or for enhancing the efficacy of existing treatments.

Towards the identification of potential infectious sites for bluetongue in Italy: a spatial analysis approach based on the distribution of Culicoides imicola.

A geographic information system (GIS) based on grids was developed by the National Reference Center for Veterinary Epidemiology at the Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise 'G. Caporale' (IZS) in Teramo to identify potential infectious sites for bluetongue (BT) disease in Italy. Geographical and climatic variables were used to build a spatial process model (SPM); the different layers were combined by sequential addition. The final grids (with a cell size of 0.0387 decimal degrees) were generated for each season of the year, and the suitability of each cell for the presence of C. imicola given a value ranking from 0 to 10. While this model more accurately predicts the presence of C. imicola in the Basilicata and Sicily regions, it still over-predicted its presence in the Puglia region. This could be due to the occurrence of calcareous soils which dominate the Puglia landscape. The present SPM is an additive model that assigns an equal weight to each variable. However, the results suggest the existence of hitherto unconsidered variables that significantly influence the prevalence of C. imicola. To reflect their importance, these variables should be assigned a higher weighting in future models. However, the decision in regard to precisely what this weighting should be depends on a very thorough knowledge of the ecology of C. imicola.