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Oxidative stress, with reactive oxygen intermediate formation, may represent a common mechanism by which liver injury is induced by diverse etiologies. Oxidative stress enhances nuclear factor kappa B (NF-kappa B) activity, and NF-kappa B activity has been shown to enhance the expression of cytotoxic cytokines. Acute hepatic injury caused by reactive oxygen intermediate production was induced by an intraperitoneal injection of CCl4 in mice. This injury was significantly inhibited by intravenous pretreatment of the mice with a water-soluble emulsion of alpha-tocopherol. Alpha-tocopherol treatment of the mice given the CCl4 also reduced the NF-kappa B binding to levels approaching those found in normal mice. In vitro treatment of a monocyte/macrophage cell line with CCl4 led to enhanced NF-kappa B binding and an increase in tumor necrosis factor-alpha (TNF-alpha) messenger RNA levels. Liver specimens taken from patients with acute fulminant hepatitis had markedly increased NF-kappa B binding activity in comparison with the binding of normal livers. These data demonstrate that abolishing acute hepatic injury with alpha-tocopherol, a free radical scavenger, also eliminated increased NF-kappa B binding. It is tempting to speculate that enhanced NF-kappa B expression caused by free radical production/oxidative stress may modulate liver injury, perhaps through an effect on cytotoxic cytokine synthesis.

Wildfire susceptibility maps display the spatial probability of an area to burn in the future, based solely on the intrinsic local proprieties of a site. Current studies in this field often rely on statistical models, often improved by expert knowledge for data retrieving and processing. In the last few years, machine learning algorithms have proven to be successful in this domain, thanks to their capability of learning from data through the modeling of hidden relationships. In the present study, authors introduce an approach based on random forests, allowing elaborating a wildfire susceptibility map for the Liguria region in Italy. This region is highly affected by wildfires due to the dense and heterogeneous vegetation, with more than 70% of its surface covered by forests, and due to the favorable climatic conditions. Susceptibility was assessed by considering the dataset of the mapped fire perimeters, spanning a 21-year period and different geo-environmental predisposing factors (i.e., land cover, vegetation type, road network, altitude, and derivatives). One main objective was to compare different models in order to evaluate the effect of: (i) including or excluding the neighboring vegetation type as additional predisposing factors and (ii) using an increasing number of folds in the spatial-cross validation procedure. Susceptibility maps for the two fire seasons were finally elaborated and validated. Results highlighted the capacity of the proposed approach to identify areas that could be affected by wildfires in the near future, as well as its goodness in assessing the efficiency of fire-fighting activities.

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