Disrupting the EMMPRIN (CD147)-cyclophilin A interaction reduces infarct size and preserves systolic function after myocardial ischemia and reperfusion.

Objective-Inflammation and proteolysis crucially contribute to myocardial ischemia and reperfusion injury. The extracellular matrix metalloproteinase inducer EMMPRIN (CD147) and its ligand cyclophilin A (CyPA) may be involved in both processes. The aim of the study was to characterize the role of the CD147 and CyPA interplay in myocardial ischemia/reperfusion (I/R) injury.Methods and Results-Immunohistochemistry showed enhanced expression of CD147 and CyPA in myocardial sections from human autopsies of patients who had died from acute myocardial infarction and from mice at 24 hours after I/R. At 24 hours and 7 days after I/R, the infarct size was reduced in CD147(+/-) mice vs CD147(+/+) mice (C57Bl/6), in mice (C57Bl/6) treated with monoclonal antibody anti-CD147 vs control monoclonal antibody, and in CyPA(-/-) mice vs CyPA(+/+) mice (129S6/SvEv), all of which are associated with reduced monocyte and neutrophil recruitment at 24 hours and with a preserved systolic function at 7 days. The combination of CyPA(-/-) mice with anti-CD147 treatment did not yield further protection compared with either inhibition strategy alone. In vitro, treatment with CyPA induced monocyte chemotaxis in a CD147-and phosphatidylinositol 3-kinase-dependent manner and induced monocyte rolling and adhesion to endothelium (human umbilical vein endothelial cells) under flow in a CD147-dependent manner.Conclusion-CD147 and its ligand CyPA are inflammatory mediators after myocardial ischemia and reperfusion and represent potential targets to prevent myocardial I/R injury.

The epidthelial sodium channel ENaC and its regulators in the epidermal permeability barrier function

The highly amiloride-sensitive epithelial sodium channel ENaC is well known to be involved in controlling whole body sodium homeostasis and lung liquid clearance. ENaC expression has also been detected in the skin of amphibians and mammals. Mice lacking ENaC expression lose rapidly weight associated with an epidermal barrier defect that develops following birth. This dehydration is accompanied with a highly abnormal lipid matrix composition and an impaired skin surface acidification. This strongly suggests a role of ENaC in the maturation of barrier function rather than in the prenatal generation of the barrier, and may be as such an important modulator for skin hydration. In parallel, gene targeting experiments of regulators of ENaC activity, membrane serine proteases, also termed channel activating proteases, like CAP1/Prss8 and matriptase/MT-SP1 by themselves have been shown to be crucial for the epidermal barrier function. In our review, we mainly focus on the role of ENaC and its regulators in the skin and discuss their importance in the epidermal permeability barrier function.

Expression and function of endothelin and its receptors in vascular adventitial fibroblasts

Objective: The adventitia has been recognized to play important roles in vascular oxidative stress, remodelling and contraction. We recently demonstrated that adventitial fibroblasts are able to express endothelin-1 (ET-1) in response to angiotensin II (ANG II). However, the mechanisms by which ANG II induces ET-1 expression are unknown. It is also unclear whether the ET-1 receptors are expressed in the adventitia. We therefore examined the role of oxidative stress in the regulation of ET-1. We also investigated the expression of both the ETA and ETB receptors and the roles of these two types of receptors in collagen synthesis and ET-1 clearance in adventitial fibroblasts. Methods and Results: Adventitial fibroblasts were isolated and cultured from the thoracic mouse aorta. Cells were treated with ANG II (lOOnM), ET-1 (lOpM), NADPH oxidase inhibitor apocynin (lOOfiM), the superoxide anion scavenger tempol (lOOfiM), the ANG II receptor antagonists (100[aM), losartan (AT| receptor) and PD 1233 19 (AT2 receptor), the ET-1 receptor antagonists (lOOuM), BQ123 (ETA receptor) and BQ788 (ETB receptor), and the ETB receptor agonist (lOOnM) Sarafotoxin 6C. ET-1 peptide levels were determined by ELISA, while ETA ,ETB and collagen levels were determined by Western blot. ANG II increased ET-1 peptide levels in a time-dependent manner reaching significance when incubated for 24 hours. NAD(P)H oxidase inhibitor, apocynin, as well as the superoxide scanverger, tempol, significantly reduced ANG Il-induced ET-1 peptide levels while over-expression of SOD1 (endogenous antioxidant enzyme) significantly decreased ANG Il-induced collagen I expression, therefore implicating reactive oxygen species in the mediation of ET-1. ANG II increased ETA receptor protein as well as collagen in a similar fashion, reaching significance after 4, 6, and 24 hours treatment. ANG II induced collagen was reduced while in the presence of the ETA receptor antagonist suggesting the role of the ETa receptor in the regulation of the extracellular matrix. ANG II treatment also increased ETB receptor protein levels in a time-dependent manner. ANG II treatment in the presence of the ETB receptor antagonist significantly increased ET-1 peptide levels. On another hand, the ETB receptor agonist, Sarafotoxin 6C, significantly decreased ET-1 peptide levels. These data implicate the role of the ETb receptor in the clearance of the ET-1 peptide. Conclusion: ANG II-induced increases of ET-1 peptide appears to be mediated by reactive oxygen species derived from NAD(P)H oxidase. Both the ETA and ETB receptors are expressed in adventitial fibroblasts. The ETA receptor subtype mediates collagen I expression, while the ETB receptor may play a protective role through increasing the clearance of the ET- 1 peptide.

Non-negative matrix decomposition approaches to frequency domain analysis of music audio signals

On étudie l’application des algorithmes de décomposition matricielles tel que la Factorisation Matricielle Non-négative (FMN), aux représentations fréquentielles de signaux audio musicaux. Ces algorithmes, dirigés par une fonction d’erreur de reconstruction, apprennent un ensemble de fonctions de base et un ensemble de coef- ficients correspondants qui approximent le signal d’entrée. On compare l’utilisation de trois fonctions d’erreur de reconstruction quand la FMN est appliquée à des gammes monophoniques et harmonisées: moindre carré, divergence Kullback-Leibler, et une mesure de divergence dépendente de la phase, introduite récemment. Des nouvelles méthodes pour interpréter les décompositions résultantes sont présentées et sont comparées aux méthodes utilisées précédemment qui nécessitent des connaissances du domaine acoustique. Finalement, on analyse la capacité de généralisation des fonctions de bases apprises par rapport à trois paramètres musicaux: l’amplitude, la durée et le type d’instrument. Pour ce faire, on introduit deux algorithmes d’étiquetage des fonctions de bases qui performent mieux que l’approche précédente dans la majorité de nos tests, la tâche d’instrument avec audio monophonique étant la seule exception importante.

Semiclassical evaluation of average nuclear one- and two-body matrix elements

Thomas-Fermi theory is developed to evaluate nuclear matrix elements averaged on the energy shell, on the basis of independent particle Hamiltonians. One- and two-body matrix elements are compared with the quantal results, and it is demonstrated that the semiclassical matrix elements, as function of energy, well pass through the average of the scattered quantum values. For the one-body matrix elements it is shown how the Thomas-Fermi approach can be projected on good parity and also on good angular momentum. For the two-body case, the pairing matrix elements are considered explicitly.

Moderate Champagne consumption promotes an acute improvement in acute endothelial-independent vascular function in healthy human volunteers

Epidemiological studies have suggested an inverse correlation between red wine consumption and the incidence of CVD. However, Champagne wine has not been fully investigated for its cardioprotective potential. In order to assess whether acute and moderate Champagne wine consumption is capable of modulating vascular function, we performed a randomised, placebo-controlled, cross-over intervention trial. We show that consumption of Champagne wine, but not a control matched for alcohol, carbohydrate and fruit-derived acid content, induced an acute change in endothelium-independent vasodilatation at 4 and 8 h post-consumption. Although both Champagne wine and the control also induced an increase in endothelium-dependent vascular reactivity at 4 h, there was no significant difference between the vascular effects induced by Champagne or the control at any time point. These effects were accompanied by an acute decrease in the concentration of matrix metalloproteinase (MMP-9), a significant decrease in plasma levels of oxidising species and an increase in urinary excretion of a number of phenolic metabolites. In particular, the mean total excretion of hippuric acid, protocatechuic acid and isoferulic acid were all significantly greater following the Champagne wine intervention compared with the control intervention. Our data suggest that a daily moderate consumption of Champagne wine may improve vascular performance via the delivery of phenolic constituents capable of improving NO bioavailability and reducing matrix metalloproteinase activity.

A tunable radial basis function model for nonlinear system identification using particle swarm optimisation

A tunable radial basis function (RBF) network model is proposed for nonlinear system identification using particle swarm optimisation (PSO). At each stage of orthogonal forward regression (OFR) model construction, PSO optimises one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is computationally more efficient.

Nonlinear system identification using particle swarm optimisation tuned radial basis function models

A novel particle swarm optimisation (PSO) tuned radial basis function (RBF) network model is proposed for identification of non-linear systems. At each stage of orthogonal forward regression (OFR) model construction process, PSO is adopted to tune one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is often more efficient in model construction. The effectiveness of the proposed PSO aided OFR algorithm for constructing tunable node RBF models is demonstrated using three real data sets.

Construction of tunable radial basis function networks using orthogonal forward selection

An orthogonal forward selection (OFS) algorithm based on leave-one-out (LOO) criteria is proposed for the construction of radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines an RBF node, namely, its center vector and diagonal covariance matrix, by minimizing the LOO statistics. For regression application, the LOO criterion is chosen to be the LOO mean-square error, while the LOO misclassification rate is adopted in two-class classification application. This OFS-LOO algorithm is computationally efficient, and it is capable of constructing parsimonious RBF networks that generalize well. Moreover, the proposed algorithm is fully automatic, and the user does not need to specify a termination criterion for the construction process. The effectiveness of the proposed RBF network construction procedure is demonstrated using examples taken from both regression and classification applications.

A fast identification algorithm for Box-Cox transformation based radial basis function neural network

In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

Modified radial basis function neural network using output transformation

A modified radial basis function (RBF) neural network and its identification algorithm based on observational data with heterogeneous noise are introduced. The transformed system output of Box-Cox is represented by the RBF neural network. To identify the model from observational data, the singular value decomposition of the full regression matrix consisting of basis functions formed by system input data is initially carried out and a new fast identification method is then developed using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator (MLE) for a model base spanned by the largest eigenvectors. Finally, the Box-Cox transformation-based RBF neural network, with good generalisation and sparsity, is identified based on the derived optimal Box-Cox transformation and an orthogonal forward regression algorithm using a pseudo-PRESS statistic to select a sparse RBF model with good generalisation. The proposed algorithm and its efficacy are demonstrated with numerical examples.

Radial basis function classifier construction using particle swarm optimisation aided orthogonal forward regression

We develop a particle swarm optimisation (PSO) aided orthogonal forward regression (OFR) approach for constructing radial basis function (RBF) classifiers with tunable nodes. At each stage of the OFR construction process, the centre vector and diagonal covariance matrix of one RBF node is determined efficiently by minimising the leave-one-out (LOO) misclassification rate (MR) using a PSO algorithm. Compared with the state-of-the-art regularisation assisted orthogonal least square algorithm based on the LOO MR for selecting fixednode RBF classifiers, the proposed PSO aided OFR algorithm for constructing tunable-node RBF classifiers offers significant advantages in terms of better generalisation performance and smaller model size as well as imposes lower computational complexity in classifier construction process. Moreover, the proposed algorithm does not have any hyperparameter that requires costly tuning based on cross validation.

Targeting myocardial remodelling to develop novel therapies for heart failure: a position paper from the Working Group on Myocardial Function of the European Society of Cardiology

The failing heart is characterized by complex tissue remodelling involving increased cardiomyocyte death, and impairment of sarcomere function, metabolic activity, endothelial and vascular function, together with increased inflammation and interstitial fibrosis. For years, therapeutic approaches for heart failure (HF) relied on vasodilators and diuretics which relieve cardiac workload and HF symptoms. The introduction in the clinic of drugs interfering with beta-adrenergic and angiotensin signalling have ameliorated survival by interfering with the intimate mechanism of cardiac compensation. Current therapy, though, still has a limited capacity to restore muscle function fully, and the development of novel therapeutic targets is still an important medical need. Recent progress in understanding the molecular basis of myocardial dysfunction in HF is paving the way for development of new treatments capable of restoring muscle function and targeting specific pathological subsets of LV dysfunction. These include potentiating cardiomyocyte contractility, increasing cardiomyocyte survival and adaptive hypertrophy, increasing oxygen and nutrition supply by sustaining vessel formation, and reducing ventricular stiffness by favourable extracellular matrix remodelling. Here, we consider drugs such as omecamtiv mecarbil, nitroxyl donors, cyclosporin A, SERCA2a (sarcoplasmic/endoplasmic Ca(2 +) ATPase 2a), neuregulin, and bromocriptine, all of which are currently in clinical trials as potential HF therapies, and discuss novel molecular targets with potential therapeutic impact that are in the pre-clinical phases of investigation. Finally, we consider conceptual changes in basic science approaches to improve their translation into successful clinical applications.

The “pain matrix” in pain-free individuals

Human functional imaging provides a correlative picture of brain activity during pain. A particular set of central nervous system structures (eg, the anterior cingulate cortex, thalamus, and insula) consistently respond to transient nociceptive stimuli causing pain. Activation of this so-called pain matrix or pain signature has been related to perceived pain intensity, both within and between individuals,1,2 and is now considered a candidate biomarker for pain in medicolegal settings and a tool for drug discovery. The pain-specific interpretation of such functional magnetic resonance imaging (fMRI) responses, although logically flawed,3,4 remains pervasive. For example, a 2015 review states that “the most likely interpretation of activity in the pain matrix seems to be pain.”4 Demonstrating the nonspecificity of the pain matrix requires ruling out the presence of pain when highly salient sensory stimuli are presented. In this study, we administered noxious mechanical stimuli to individuals with congenital insensitivity to pain and sampled their brain activity with fMRI. Loss-of-function SCN9A mutations in these individuals abolishes sensory neuron sodium channel Nav1.7 activity, resulting in pain insensitivity through an impaired peripheral drive that leaves tactile percepts fully intact.5 This allows complete experimental disambiguation of sensory responses and painful sensations

A Time Efficient Optical Model for GATE Simulation of a LYSO Scintillation Matrix Used in PET Applications

A time efficient optical model is proposed for GATE simulation of a LYSO scintillation matrix coupled to a photomultiplier. The purpose is to avoid the excessively long computation time when activating the optical processes in GATE. The usefulness of the model is demonstrated by comparing the simulated and experimental energy spectra obtained with the dual planar head equipment for dosimetry with a positron emission tomograph ( DoPET). The procedure to apply the model is divided in two steps. Firstly, a simplified simulation of a single crystal element of DoPET is used to fit an analytic function that models the optical attenuation inside the crystal. In a second step, the model is employed to calculate the influence of this attenuation in the energy registered by the tomograph. The use of the proposed optical model is around three orders of magnitude faster than a GATE simulation with optical processes enabled. A good agreement was found between the experimental and simulated data using the optical model. The results indicate that optical interactions inside the crystal elements play an important role on the energy resolution and induce a considerable degradation of the spectra information acquired by DoPET. Finally, the same approach employed by the proposed optical model could be useful to simulate a scintillation matrix coupled to a photomultiplier using single or dual readout scheme.