Annexin A2-Mediated Plasminogen Activation in Endothelial Cells Contributes to the Proangiogenic Effect of Adenosine A2A Receptors.

Adenosine A2A receptor mediates the promotion of wound healing and revascularization of injured tissue, in healthy and animals with impaired wound healing, through a mechanism depending upon tissue plasminogen activator (tPA), a component of the fibrinolytic system. In order to evaluate the contribution of plasmin generation in the proangiogenic effect of adenosine A2A receptor activation, we determined the expression and secretion of t-PA, urokinase plasminogen activator (uPA), plasminogen activator inhibitor-1 (PAI-1) and annexin A2 by human dermal microvascular endothelial cells stimulated by the selective agonist CGS-21680. The plasmin generation was assayed through an enzymatic assay and the proangiogenic effect was studied using an endothelial tube formation assay in Matrigel. Adenosine A2A receptor activation in endothelial cells diminished the release of PAI-1 and promoted the production of annexin A2, which acts as a cell membrane co-receptor for plasminogen and its activator tPA. Annexin A2 mediated the increased cell membrane-associated plasmin generation in adenosine A2A receptor agonist treated human dermal microvascular endothelial cells and is required for tube formation in an in vitro model of angiogenesis. These results suggest a novel mechanism by which adenosine A2A receptor activation promotes angiogenesis: increased endothelial expression of annexin A2, which, in turn, promotes fibrinolysis by binding tPA and plasminogen to the cell surface.

Síndrome de Mazabraud. Nuevo caso clínico y revisión de los hallazgos / Mazabraud's syndrome. New clinical case and review of findings

Los mixomas intramusculares son tumores benignos y poco frecuentes, que se presentan predominantemente en los miembros inferiores. Más infrecuente es la asociación de mixomas y de displasia fibrosa, generalmente poliostótica. Esta asociación se conoce como síndrome de Mazabraud, de la que se han descrito aproximadamente 81 casos en la literatura. Presentamos un nuevo caso de esta rara asociación, para enfatizar la importancia de reconocer este síndrome en el diagnóstico y manejo adecuado del paciente (AU)

Intramuscular myxomas are benign and rare tumors that affects predominantly the lower limbs. The association of myxomas and fibrous dysplasia, usually polyostotic, is rarer. This association is known as Mazabraud's syndrome, of which about 81 cases have been described in the literature. We present a new case of this uncommon association to emphasise the importance of recognizing this syndrome in the diagnosis and appropriate management of the patient (AU)

Mazabraud's syndrome. New clinical case and review of findings. / Síndrome de Mazabraud. Nuevo caso clínico y revisión de los hallazgos.

Intramuscular myxomas are benign and rare tumors that affects predominantly the lower limbs. The association of myxomas and fibrous dysplasia, usually polyostotic, is rarer. This association is known as Mazabraud's syndrome, of which about 81 cases have been described in the literature. We present a new case of this uncommon association to emphasise the importance of recognizing this syndrome in the diagnosis and appropriate management of the patient.

Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: a case study in an agricultural setting (Southern Spain).

Watershed management decisions need robust methods, which allow an accurate predictive modeling of pollutant occurrences. Random Forest (RF) is a powerful machine learning data driven method that is rarely used in water resources studies, and thus has not been evaluated thoroughly in this field, when compared to more conventional pattern recognition techniques key advantages of RF include: its non-parametric nature; high predictive accuracy; and capability to determine variable importance. This last characteristic can be used to better understand the individual role and the combined effect of explanatory variables in both protecting and exposing groundwater from and to a pollutant. In this paper, the performance of the RF regression for predictive modeling of nitrate pollution is explored, based on intrinsic and specific vulnerability assessment of the Vega de Granada aquifer. The applicability of this new machine learning technique is demonstrated in an agriculture-dominated area where nitrate concentrations in groundwater can exceed the trigger value of 50 mg/L, at many locations. A comprehensive GIS database of twenty-four parameters related to intrinsic hydrogeologic proprieties, driving forces, remotely sensed variables and physical-chemical variables measured in "situ", were used as inputs to build different predictive models of nitrate pollution. RF measures of importance were also used to define the most significant predictors of nitrate pollution in groundwater, allowing the establishment of the pollution sources (pressures). The potential of RF for generating a vulnerability map to nitrate pollution is assessed considering multiple criteria related to variations in the algorithm parameters and the accuracy of the maps. The performance of the RF is also evaluated in comparison to the logistic regression (LR) method using different efficiency measures to ensure their generalization ability. Prediction results show the ability of RF to build accurate models with strong predictive capabilities.

Using semivariogram parameter uncertainty in hydrogeological applications.

Geostatistical estimation (kriging) and geostatistical simulation are routinely used in ground water hydrology for optimal spatial interpolation and Monte Carlo risk assessment, respectively. Both techniques are based on a model of spatial variability (semivariogram or covariance) that generally is not known but must be inferred from the experimental data. Where the number of experimental data is small (say, several tens), as is not unusual in ground water hydrology, the model fitted to the empirical semivariogram entails considerable uncertainty. If all the practical results are based on this unique fitted model, the final results will be biased. We propose that, instead of using a unique semivariogram model, the full range of models that are inside a given confidence region should be used, and the weight that each semivariogram model has on the final result should depend on its plausibility. The first task, then, is to evaluate the uncertainty of the model, which can be efficiently done by using maximum likelihood inference. The second task is to use the range of plausible models in applications and to show the effect observed on the final results. This procedure is put forth here with kriging and simulation applications, where the uncertainty in semivariogram parameters is propagated into the final results (e.g., the prediction of ground water head). A case study using log-transmissivity data from the Vega de Granada aquifer, in southern Spain, is given to illustrate the methodology.