MELD score and older age were independent predictors of mortality. Age increased the discriminatory ability of MELD score to predict death. This new model may be useful for stratifying patients in future therapeutic trials, deserving further validation.

No abstract

Multi-electrode soil electrical resistivity (ρ) tomography was used for the non-invasive study of tree roots in situ and their spatial distribution in an agricultural soil. The quantitative relations of ρ and root biometry and the contribution of different root size classes were investigated with two-and three-dimensional 48-electrode tomograms in an orchard in southern Italy on a Typic haploxeralf fine, mixed termic soil. Root biomass density (RD) and root length density (RLD) were measured destructively on coarse (>2 mm diameter) and fine roots, and soil paste electrical conductivity, water content, stone content, texture, organic matter and pH were measured on soil samples taken up to 0.48-m deep. Areas of large ρ values (up to 460 ohm m) were found close to tree trunks and variability in ρ was related to RD (0-0.137 Mg m −3 ) only; the resistive response was from coarse roots. The effect of other soil variables on ρ was overshadowed by the presence of roots and therefore no significant multivariate relationship was found. A highly significant ρ-RD gamma GLM model used to fit positively skewed data provides a useful framework for regression analysis when ρ is dominated by roots. Soil electrical resistivity is promising as a proxy for RD in orchards, but not for RLD, and the effect of tree roots on ρ needs to be taken into account in electrical surveys of soils.

he objective of this research was to assess the effects of different tillage systems on the spatial and temporal variation of soil resistivity and soil features related to resistance to penetration and porosity using Electrical Resistivity Tomography (ERT). Two-dimensional (vertical and horizontal) ERT was performed on long-term conventional deep tillage (CT), minimum tillage (MT), no-tillage (NT), and by tilling a no-till plot (freshly tilled no-till [FTNT]). The tillage treatments were compared in two different studies with measurements taken at different scale and with two different sampling configurations. The first study consisted of ERT measured on a 5.75 m linear transect with horizontal and vertical high resolution measurements and a second study performed at the field scale using an on-the-go automatic resistivity profile. The on-the-go equipment collected data simultaneously at three different depths (50, 100, 200 cm) and data were referenced by differential global positioning systems (DGPS). Total variation in soil resistivity was significantly explained by tillage treatment and soil depth and by their interaction. The response of soil resistivity to tillage was able to significantly discern between tilled and untilled soil, and between FTNT and the old tillage. Soil resistance to penetration also allowed to detect highly significant differences between the untilled and other treatments at 5 cm, but did not discriminate between FTNT and the other tilled treatments, due to high variability. The automatic resistivity profiling (ARP) measurements were affected by fresh tillage, given the strong response of resistivity to soil bulk density for the first layer

The length of hospital stay (LOS) is an important measure of efficiency in the use of hospital resources. Acute Myocardial Infarction (AMI), as one of the diseases with higher mortality and LOS variability in the OECD countries, has been studied with predominant use of administrative data, particularly on mortality risk adjustment, failing investigation in the resource planning and specifically in LOS. This paper presents results of a predictive model for extended LOS (LOSE - above 75th percentile of LOS) using both administrative and clinical data, namely laboratory data, in order to develop a decision support system. Laboratory and administrative data of a Portuguese hospital were included, using logistic regression to develop this predictive model. A model with three laboratory data and seven administrative data variables (six comorbidities and age ≥ 69 years), with excellent discriminative ability and a good calibration, was obtained. The model validation shows also good results. Comorbidities were relevant predictors, mainly diabetes with complications, showing the highest odds of LOSE (OR = 37,83; p = 0,001). AMI patients with comorbidities (diabetes with complications, cerebrovascular disease, shock, respiratory infections, pulmonary oedema), with pO2 above level, aged 69 years or older, with cardiac dysrhythmia, neutrophils above level, pO2 below level, and prothrombin time above level, showed increased risk of extended LOS. Our findings are consistent with studies that refer these variables as predictors of increased risk.