A randomized clinical trial of hydroxymethylglutaryl-Coenzyme A reductase inhibition for Acute Lung Injury (The HARP study)

Rationale: There is no effective pharmacological treatment for acute lung injury (ALI). Statins are a potential new therapy because they modify many of the underlying processes important in ALI.

Objectives: To test whether simvastatin improves physiological and biological outcomes in ALI.

Methods: We conducted a randomized, double-blinded, placebo-controlled trial in patients with ALI. Patients received 80 mg simvastatin or placebo until cessation of mechanical ventilation or up to 14 days. Extravascular lung water was measured using thermodilution. Measures of pulmonary and nonpulmonary organ function were assessed daily. Pulmonary and systemic inflammation was assessed by bronchoalveolar lavage fluid and plasma cytokines. Systemic inflammation was also measured by plasma C-reactive protein.

Measurements and Main Results: Sixty patients were recruited. Baseline characteristics, including demographics and severity of illness scores, were similar in both groups. At Day 7, there was no difference in extravascular lung water. By Day 14, the simvastatin-treated group had improvements in nonpulmonary organ dysfunction. Oxygenation and respiratory mechanics improved, although these parameters failed to reach statistical significance. Intensive care unit mortality was 30% in both groups. Simvastatin was well tolerated, with no increase in adverse events. Simvastatin decreased bronchoalveolar lavage IL-8 by 2.5-fold (P = 0.04). Plasma C-reactive protein decreased in both groups but failed to achieve significance in the placebo-treated group.

Conclusions: Treatment with simvastatin appears to be safe and may be associated with an improvement in organ dysfunction in ALI. These clinical effects may be mediated by a reduction in pulmonary and systemic inflammation.

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.

A fast multi-output RBF neural network construction method

This paper investigates the center selection of multi-output radial basis function (RBF) networks, and a multi-output fast recursive algorithm (MFRA) is proposed. This method can not only reveal the significance of each candidate center based on the reduction in the trace of the error covariance matrix, but also can estimate the network weights simultaneously using a back substitution approach. The main contribution is that the center selection procedure and the weight estimation are performed within a well-defined regression context, leading to a significantly reduced computational complexity. The efficiency of the algorithm is confirmed by a computational complexity analysis, and simulation results demonstrate its effectiveness. (C) 2010 Elsevier B.V. All rights reserved.

Two-stage mixed discrete-continuous identification of radial basis function (RBF) neural models for nonlinear systems

The identification of nonlinear dynamic systems using radial basis function (RBF) neural models is studied in this paper. Given a model selection criterion, the main objective is to effectively and efficiently build a parsimonious compact neural model that generalizes well over unseen data. This is achieved by simultaneous model structure selection and optimization of the parameters over the continuous parameter space. It is a mixed-integer hard problem, and a unified analytic framework is proposed to enable an effective and efficient two-stage mixed discrete-continuous; identification procedure. This novel framework combines the advantages of an iterative discrete two-stage subset selection technique for model structure determination and the calculus-based continuous optimization of the model parameters. Computational complexity analysis and simulation studies confirm the efficacy of the proposed algorithm.

Neural Network Modeling of Rheological Parameters of Grouts Containing Viscosity-Modifying Agent

The development of artificial neural network (ANN) models to predict the rheological behavior of grouts is described is this paper and the sensitivity of such parameters to the variation in mixture ingredients is also evaluated. The input parameters of the neural network were the mixture ingredients influencing the rheological behavior of grouts, namely the cement content, fly ash, ground-granulated blast-furnace slag, limestone powder, silica fume, water-binder ratio (w/b), high-range water-reducing admixture, and viscosity-modifying agent (welan gum). The six outputs of the ANN models were the mini-slump, the apparent viscosity at low shear, and the yield stress and plastic viscosity values of the Bingham and modified Bingham models, respectively. The model is based on a multi-layer feed-forward neural network. The details of the proposed ANN with its architecture, training, and validation are presented in this paper. A database of 186 mixtures from eight different studies was developed to train and test the ANN model. The effectiveness of the trained ANN model is evaluated by comparing its responses with the experimental data that were used in the training process. The results show that the ANN model can accurately predict the mini-slump, the apparent viscosity at low shear, the yield stress, and the plastic viscosity values of the Bingham and modified Bingham models of the pseudo-plastic grouts used in the training process. The results can also predict these properties of new mixtures within the practical range of the input variables used in the training with an absolute error of 2%, 0.5%, 8%, 4%, 2%, and 1.6%, respectively. The sensitivity of the ANN model showed that the trend data obtained by the models were in good agreement with the actual experimental results, demonstrating the effect of mixture ingredients on fluidity and the rheological parameters with both the Bingham and modified Bingham models.

Self-generated expressions of residual complaints following brain injury

Following brain injury there is often a prolonged period of deteriorating psychological condition, despite neurological stability or improvement. This is presumably consequent to the remission of anosognosia and the realisation of permanently worsened status. This change is hypothesised to be directed partially by the socially mediated processes which play a role in generating self-awareness and which here direct the reconstruction of the self as a permanently injured person. However, before we can understand this process of redevelopment, we need an unbiassed technique to monitor self-awareness. Semi-structured interviews were conducted with 30 individuals with long-standing brain injuries to capture their spontaneous complaints and their level of insight into the implications of their difficulties. The focus was on what the participants said in their own words, and the extent to which self-knowledge of difficulties was spontaneously salient to the participants. Their responses were subjected to content analysis. Most participants were able to say that they had brain injuries and physical difficulties, many mentioned memory and attentional problems and a few made references to a variety of emotional disturbances. Content analysis of data from unbiassed interviews can reveal the extent to which people with brain injuries know about their difficulties. Social constructionist accounts of self-awareness and recovery are supported.

Recovery from brain injury: Finding the missing bits of the puzzle

Background: One basic problem found during rehabilitation is that people with brain injuries lack awareness of their difficulties. Research into this phenomenon has often disregarded the voices of those affected by the trauma and do not give an insider's perspective on the process through which a person with a brain injury develops awareness of their difficulties.

A comparative study of nursing attitudes towards young male survivors of brain injury: A questionnaire survey.

Background The attitudes members of the nursing profession hold towards survivors of brain injury may impact on the level of help, and degree of involvement they are willing to have. Given that the manner in which an individual receives their brain injury has been shown to impact on public prejudices, the importance of exploring nursing attitudes to this vulnerable group, and the subsequent impact this may have on the caring role, requires investigation. Objective To investigate the attitudes held by members of the nursing profession towards young male survivors of brain injury whose behaviour either contributed, or did not contribute, to their injury. Design Independent groups design. Setting and participants Ninety trainee and sixty-nine qualified nurses respectively drawn from a university in the south west of England and the emergency, orthopaedic and paediatric Departments of the Royal Devon and Exeter Hospital, UK. Methods Participants were randomly assigned to one of four fictional brain injury scenarios. A young male character was portrayed as sustaining a brain injury as a result of either an aneurysm, or through drug taking, with their behaviour being either a contributory or non-contributory factor. On reading these, participants were asked to complete the prejudicial evaluation scale, the social interaction scale and the helping behaviour scale. Results Analysis of variance showed that qualified nurses held more prejudicial attitudes than student nurses towards survivors of brain injury. Mean scores indicated that individuals seen as contributing towards their injury were likely to experience more prejudice (blame total = 42.35 vs. no blame total = 38.34), less social interaction (blame total = 37.54 vs. no blame total = 41.10), and less helping behaviour (blame total = 21.49 vs. no blame total = 22.34) by both groups. Conclusions Qualified nurses should be mindful of the impact their attitudes and judgements of survivors of brain injury may have on the subsequent care they provide. Greater emphasis on the effects of negative attitudes on patient interactions during training may provide nurses with the understanding to recognise and avoid challenges to their caring role in the future.

Neural network forecasts of input-output technology

A significant part of the literature on input-output (IO) analysis is dedicated to the development and application of methodologies forecasting and updating technology coefficients and multipliers. Prominent among such techniques is the RAS method, while more information demanding econometric methods, as well as other less promising ones, have been proposed. However, there has been little interest expressed in the use of more modern and often more innovative methods, such as neural networks in IO analysis in general. This study constructs, proposes and applies a Backpropagation Neural Network (BPN) with the purpose of forecasting IO technology coefficients and subsequently multipliers. The RAS method is also applied on the same set of UK IO tables, and the discussion of results of both methods is accompanied by a comparative analysis. The results show that the BPN offers a valid alternative way of IO technology forecasting and many forecasts were more accurate using this method. Overall, however, the RAS method outperformed the BPN but the difference is rather small to be systematic and there are further ways to improve the performance of the BPN.