Effect of oxygen plasma on the optical properties of monolayer graphene

10.4028/www.scientific.net/AMR.896.510Advanced Materials Research896510-51

In vitro galactose impairs energy metabolism in the brain of young rats: protective role of antioxidants

We, herein, investigated the in vitro effects of galactose on the activity of pyruvate kinase, succinate dehydrogenase (SDH), complex II and IV (cytochrome c oxidase) of the respiratory chain and Na(+)K+-ATPase in the cerebral cortex, cerebellum and hippocampus of 30-day-old rats. We also determined the influence of the antioxidants, trolox, ascorbic acid and glutathione, on the effects elicited by galactose. Galactose was added to the assay at concentrations of 0.1, 3.0, 5.0 and 10.0 mM. Control experiments were performed without galactose. Galactose, at 3.0, 5.0 and 10.0 mM, decreased pyruvate kinase activity in the cerebral cortex and at 10.0 mM in the hippocampus. Galactose, at 10.0 mM, reduced SDH and complex II activities in the cerebellum and hippocampus, and reduced cytochrome c oxidase activity in the hippocampus. Additionally, decreased Na(+)K(+)-ATPase activity in the cerebral cortex and hippocampus; conversely, galactose, at 3.0 and 5.0 mM, increased this enzyme's activity in the cerebellum. Data show that galactose disrupts energy metabolism and trolox, ascorbic acid and glutathione addition prevented the majority of alterations in the parameters analyzed, suggesting the use of antioxidants as an adjuvant therapy in Classic galactosemia

Predicting septic shock outcomes in a database with missing data using fuzzy modeling : influence of pre-processing techniques on real world data-based classification

Real-world databases often contain missing data and existing correction algorithms deliver varying performance. Also, most modeling techniques are not suitable to deal with them automatically. In this study we examine different approaches to predicting septic shock in the presence of missing data. Some preprocessing techniques for managing missing data include disregarding data, or replacing it with information that by design introduces bias. In this study, we show that predictive performance improves by employing a minimum pre-processing technique, the Zero-Order-Hold (ZOH) method, by applying a Fuzzy C-Means clustering technique based on the partial distance calculation strategy (FCM-PDS) and by computing the final classification regarding the samples from each patient. Performance improvements continue to occur where up to approximately 60% of the data is missing, though for higher percentage the classification performance still is statistically improved. We further validate this approach by making comparisons with previous studies