Reduced Nitro-oxidative Stress and Neural Cell Death Suggests a Protective Role for Microglial Cells in TNFα−/− Mice in Ischemic Retinopathy

Purpose. Neovascularization occurs in response to tissue ischemia and growth factor stimulation. In ischemic retinopathies, however, new vessels fail to restore the hypoxic tissue; instead, they infiltrate the transparent vitreous. In a model of oxygen-induced retinopathy (OIR), TNFa and iNOS, upregulated in response to tissue ischemia, are cytotoxic and inhibit vascular repair. The aim of this study was to investigate the mechanism for this effect.

Methods. Wild-type C57/BL6 (WT) and TNFa-/- mice were subjected to OIR by exposure to 75% oxygen (postnatal days 7–12). The retinas were removed during the hypoxic phase of the model. Retinal cell death was determined by TUNEL staining, and the microglial cells were quantified after Z-series capture with a confocal microscope. In situ peroxynitrite and superoxide were measured by using the fluorescent dyes DCF and DHE. iNOS, nitrotyrosine, and arginase were analyzed by real-time PCR, Western blot analysis, and activity determined by radiolabeled arginine conversion. Astrocyte coverage was examined after GFAP immunostaining.

Results. The TNFa-/- animals displayed a significant reduction in TUNEL-positive apoptotic cells in the inner nuclear layer of the avascular retina compared with that in the WT control mice. The reduction coincided with enhanced astrocytic survival and an increase in microglial cells actively engaged in phagocytosing apoptotic debris that displayed low ROS, RNS, and NO production and high arginase activity.

Conclusions. Collectively, the results suggest that improved vascular recovery in the absence of TNFa is associated with enhanced astrocyte survival and that both phenomena are dependent on preservation of microglial cells that display an anti-inflammatory phenotype during the early ischemic phase of OIR.

Short, medium and long term load forecasting model and virtual load forecaster based on radial basis neural networks

Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications. However, they are not typically used in medium and long term load forecasting (MLTLF) electric power models because of the difficulties associated with collecting and processing the necessary data. Virtual instrument (VI) techniques can be applied to electric power load forecasting but this is rarely reported in the literature. In this paper, we investigate the modelling and design of a VI for short, medium and long term load forecasting using ANNs. Three ANN models were built for STLF of electric power. These networks were trained using historical load data and also considering weather data which is known to have a significant affect of the use of electric power (such as wind speed, precipitation, atmospheric pressure, temperature and humidity). In order to do this a V-shape temperature processing model is proposed. With regards MLTLF, a model was developed using radial basis function neural networks (RBFNN). Results indicate that the forecasting model based on the RBFNN has a high accuracy and stability. Finally, a virtual load forecaster which integrates the VI and the RBFNN is presented.

Will Antiangiogenic Agents be a Future for Mesothelioma Therapy?

Background: Malignant mesothelioma (MM) is an aggressive disease that is diagnosed mostly in locally advanced or metastatic stage. In this condition chemotherapy with the combination cisplatin and pemetrexed or ralitrexed represents the standard treatment as supported by a phase III study. However, chemotherapy has very limited effect on the improvement of survival of patients and very few of the MM patients survive more than 2 years. A better understanding of molecular mechanisms and pathways involved in angiogenesis in MM is the basis for the development of new drugs targeted against these pathways responsible for the proliferation and survival of tumor cells.