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BackgroundThe renin-angiotensin system (RAS) plays a role in the pathogenesis of ARDS, Angiotensin II (Ang-II) contributing to the pathogenesis of inflammation and fibrogenesis. Angiotensin-(1-7) (Ang-(1-7)) may antagonize the effects of Ang-II. This study was aimed at evaluating the potential for Ang-(1-7) to reduce injury, inflammation and fibrosis in an experimental model of ARDS in the acute and late phases.MethodsMale Sprague Dawley rats underwent an instillation of 0.1 M hydrochloric acid (HCl, 2.5 ml/kg) into the right bronchus. In an acute ARDS study, acid-injured rats were subjected to high stretch mechanical ventilation (18 ml/kg) for 5 h and randomized to receive an intravenous infusion of either vehicle (saline), Ang-(1-7) at low dose(0.27 μg/kg/h) (ALD), or high dose (60 μg/kg/h) (AHD) starting simultaneously with injury or 2 h afterwards. Arterial blood gas analysis and bronchoalveolar lavage (BAL) were performed to assess the injury. For the late ARDS study, after HCl instillation rats were randomized to either vehicle or high dose Ang-(1-7) (300 μg/kg/day) infused by mini osmotic pumps for two weeks, and lung hydroxyproline content measured.ResultsIn the acute ARDS study, Ang-(1-7) led to a significant improvement in oxygenation (PaO2/FiO2 : vehicle 359 ± 86; ALD 436 ± 72; AHD 44 442 ± 56; ANOVA p = 0.007) and reduced white blood cells counts (vehicle 4,519 ± 2,234; ALD 2,496 ± 621; AHD 2,744 ± 119/mm3; ANOVA p = 0.004). Only treatment with high dose Ang-(1-7) reduced inflammatory cell numbers in BAL (vehicle 127 ± 34; AHD 96 ± 34/ μl; p = 0.033). Interestingly also delayed administration of Ang-(1-7) was effective in reducing injury. In later ARDS, Ang-(1-7) decreased hydroxyproline content (649 ± 202 and 1,117 ± 297 μg/lung; p < 0.05).ConclusionsAngiotensin-(1-7), decreased the severity of acute lung injury and inflammation induced by combined acid aspiration and high stretch ventilation. Furthermore, continuous infusion of Ang-(1-7) reduced lung fibrosis 2 weeks following acid aspiration injury. These results call for further research on Ang-(1-7) as possible therapy for ARDS.

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Patients with hip osteoarthritis demonstrate limited range of motion, muscle weakness and altered biomechanics; however, few studies have evaluated the relationships between physical impairments and movement asymmetries. The purpose of this study was to identify the physical impairments related to movement abnormalities in patients awaiting total hip arthroplasty. We hypothesized that muscle weakness and pain would be related to greater movement asymmetries. Fifty-six subjects who were awaiting total hip arthroplasty were enrolled. Pain was assessed using a 0 to 10 scale, range of motion was assessed with the Harris Hip Score and isometric hip abductor strength was tested using a hand-held dynamometer. Trunk, pelvis and hip angles and moments in the frontal and sagittal planes were measured during walking using three dimensional motion analysis. During gait, subjects had 3.49 degrees less peak hip flexion and 8.82 degrees less extension angles (p<0.001) and had 0.03 Nm/k*m less hip abduction moment on the affected side (p=0.043). Weaker hip muscles were related to greater pelvis (r=−0.291) and trunk (r=−0.332) rotations in the frontal plane. These findings suggest that hip weakness drives abnormal movement patterns at the pelvis and trunk in patients with hip osteoarthritis to a greater degree than hip pain.

The huge amount of textual data on the Web has grown in the last few years rapidly creating unique contents of massive dimension. In a decision making context, one of the most relevant tasks is polarity classification of a text source, which is usually performed through supervised learning methods. Most of the existing approaches select the best classification model leading to over-confident decisions that do not take into account the inherent uncertainty of the natural language. In this paper, we pursue the paradigm of ensemble learning to reduce the noise sensitivity related to language ambiguity and therefore to provide a more accurate prediction of polarity. The proposed ensemble method is based on Bayesian Model Averaging, where both uncertainty and reliability of each single model are taken into account. We address the classifier selection problem by proposing a greedy approach that evaluates the contribution of each model with respect to the ensemble. Experimental results on gold standard datasets show that the proposed approach outperforms both traditional classification and ensemble methods.