Parenteral Lipid Dose Restriction With Soy Oil, Not Fish Oil, Preserves Retinal Function in Neonatal Piglets.

BACKGROUND: A dietary supply of docosahexaenoic acid (DHA) and arachidonic acid (AA) is critical for neonatal retinal development. Both are absent/minimal in parenteral nutrition (PN) using soy-oil emulsions ([SO] Intralipid®) traditionally used for neonatal intestinal failure. In contrast, fish-oil emulsions ([FO] Omegaven®) are enriched in DHA/AA. The aim of this study was to compare retinal function and fatty acid content in neonatal piglets fed PN with SO or FO. METHODS: Two-5-day-old piglets were randomly allocated to SO (n = 4) or FO (n = 4), provided at equivalent doses (5g/kg/d). After 14 days of PN, retinal function was assessed by electroretinography and retinas were harvested for fatty acid content analysis. Sow-fed piglets served as a reference (REF). RESULTS: Light flash-elicited stoppage of cone and rod dark-currents (a-waves) and the ensuing postsynaptic activation of cone and rod ON bipolar cells (b-waves) were comparable between SO and REF. Responses recorded from FO were subnormal (P <0.001) when compared with both SO and REF. Retinal DHA content was similar in both groups (FO, 14.59% vs SO, 12.22%; P = 0.32); while AA was lower in FO (FO, 6.01% vs SO, 8.21%; P = .001). CONCLUSION: Paradoxically, FO containing more DHA and AA did not preserve retinal function when compared with the same low dose of SO. This may be due to the reduced AA enrichment in the retina with FO treatment. Further investigation into the ideal amounts of DHA and AA for optimal neonatal retinal function is required.

Estimating airborne pollutant concentrations in vegetated urban sites using statistical models with microclimate and urban geometry parameters as predictor variables: a case study in the city of Athens Greece.

The present study demonstrates the efficiency of applying statistical models to estimate airborne pollutant concentrations in urban vegetation by using as predictor variables readily available or easily accessible data. Results revealed that airborne cadmium concentrations in vegetation showed a predictable response to wind conditions and to various urban landscape features such as the distance between the vegetation and the adjacent street, the mean height of the adjacent buildings, the mean density of vegetation between vegetation and the adjacent street and the mean height of vegetation. An artificial neural network (ANN) model was found to have superiority in terms of accuracy with an R(2) value on the order of 0.9. The lowest R(2) value (on the order of 0.7) was associated with the linear model (SMLR model). The linear model with interactions (SMLRI model) and the tree regression (RTM) model gave similar results in terms of accuracy with R(2) values on the order of 0.8. The improvement of the results with the use of the non-linear models (RTM and ANN) and the inclusion of interaction terms in the SMLRI model implied the nonlinear relationships of pollutant concentrations to the selected predictors and showed the importance of the interactions between the various predictor variables. Despite the limitations of the models, some of them appear to be promising alternatives to multimedia-based simulation modeling approaches in urban areas with vegetation, where the application of typical deposition models is sometimes limited.