604 research outputs found

    Subcellular heterogeneity of ryanodine receptor properties in ventricular myocytes with low T-tubule density

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    Rationale: In ventricular myocytes of large mammals, not all ryanodine receptor (RyR) clusters are associated with T-tubules (TTs); this fraction increases with cellular remodeling after myocardial infarction (MI). Objective: To characterize RyR functional properties in relation to TT proximity, at baseline and after MI. Methods: Myocytes were isolated from left ventricle of healthy pigs (CTRL) or from the area adjacent to a myocardial infarction (MI). Ca2+ transients were measured under whole-cell voltage clamp during confocal linescan imaging (fluo-3) and segmented according to proximity of TTs (sites of early Ca2+ release, F>F50 within 20 ms) or their absence (delayed areas). Spontaneous Ca2+ release events during diastole, Ca2+ sparks, reflecting RyR activity and properties, were subsequently assigned to either category. Results: In CTRL, spark frequency was higher in proximity of TTs, but spark duration was significantly shorter. Block of Na+/Ca2+ exchanger (NCX) prolonged spark duration selectively near TTs, while block of Ca2+ influx via Ca2+ channels did not affect sparks properties. In MI, total spark mass was increased in line with higher SR Ca2+ content. Extremely long sparks (>47.6 ms) occurred more frequently. The fraction of near-TT sparks was reduced; frequency increased mainly in delayed sites. Increased duration was seen in near-TT sparks only; Ca2+ removal by NCX at the membrane was significantly lower in MI. Conclusion: TT proximity modulates RyR cluster properties resulting in intracellular heterogeneity of diastolic spark activity. Remodeling in the area adjacent to MI differentially affects these RyR subpopulations. Reduction of the number of sparks near TTs and reduced local NCX removal limit cellular Ca2+ loss and raise SR Ca2+ content, but may promote Ca2+ waves

    Max-and-Smooth: a two-step approach for approximate Bayesian inference in latent Gaussian models

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    This is the final version. Available on open access from International Society for Bayesian Analysis (ISBA) via the DOI in this record. With modern high-dimensional data, complex statistical models are necessary, requiring computationally feasible inference schemes. We introduce Max-and-Smooth, an approximate Bayesian inference scheme for a flexible class of latent Gaussian models (LGMs) where one or more of the likelihood parameters are modeled by latent additive Gaussian processes. Max-and-Smooth consists of two-steps. In the first step (Max), the likelihood function is approximated by a Gaussian density with mean and covariance equal to either (a) the maximum likelihood estimate and the inverse observed information, respectively, or (b) the mean and covariance of the normalized likelihood function. In the second step (Smooth), the latent parameters and hyperparameters are inferred and smoothed with the approximated likelihood function. The proposed method ensures that the uncertainty from the first step is correctly propagated to the second step. Since the approximated likelihood function is Gaussian, the approximate posterior density of the latent parameters of the LGM (conditional on the hyperparameters) is also Gaussian, thus facilitating efficient posterior inference in high dimensions. Furthermore, the approximate marginal posterior distribution of the hyperparameters is tractable, and as a result, the hyperparameters can be sampled independently of the latent parameters. In the case of a large number of independent data replicates, sparse precision matrices, and high-dimensional latent vectors, the speedup is substantial in comparison to an MCMC scheme that infers the posterior density from the exact likelihood function. The proposed inference scheme is demonstrated on one spatially referenced real dataset and on simulated data mimicking spatial, temporal, and spatio-temporal inference problems. Our results show that Max-and-Smooth is accurate and fast.NER

    Hepatitis C virus infection upregulates CD55 expression on the hepatocyte surface and promotes association with virus particles

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    CD55 limits excessive complement activation on the host cell surface by accelerating the decay of C3 convertases. In this study, we observed that hepatitis C virus (HCV) infection of hepatocytes or HCV core protein expression in transfected hepatocytes upregulated CD55 expression at the mRNA and protein levels. Further analysis suggested that the HCV core protein or full-length (FL) genome enhanced CD55 promoter activity in a luciferase-based assay, which was further augmented in the presence of interleukin-6. Mutation of the CREB or SP-1 binding site on the CD55 promoter impaired HCV core protein-mediated upregulation of CD55. HCV-infected or core protein-transfected Huh7.5 cells displayed greater viability in the presence of CD81 and CD55 antibodies and complement. Biochemical analysis revealed that CD55 was associated with cell culture-grown HCV after purification by sucrose density gradient ultracentrifugation. Consistent with this, a polyclonal antibody to CD55 captured cell culture-grown HCV. Blocking antibodies against CD55 or virus envelope glycoproteins in the presence of normal human serum as a source of complement inhibited HCV infection. The inhibition was enhanced in the presence of both the antibodies and serum complement. Collectively, these results suggest that HCV induces and associates with a negative regulator of the complement pathway, a likely mechanism for immune evasion

    Extent of non-publication in cohorts of studies approved by research ethics committees or included in trial registries

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    BACKGROUND: The synthesis of published research in systematic reviews is essential when providing evidence to inform clinical and health policy decision-making. However, the validity of systematic reviews is threatened if journal publications represent a biased selection of all studies that have been conducted (dissemination bias). To investigate the extent of dissemination bias we conducted a systematic review that determined the proportion of studies published as peer-reviewed journal articles and investigated factors associated with full publication in cohorts of studies (i) approved by research ethics committees (RECs) or (ii) included in trial registries. METHODS AND FINDINGS: Four bibliographic databases were searched for methodological research projects (MRPs) without limitations for publication year, language or study location. The searches were supplemented by handsearching the references of included MRPs. We estimated the proportion of studies published using prediction intervals (PI) and a random effects meta-analysis. Pooled odds ratios (OR) were used to express associations between study characteristics and journal publication. Seventeen MRPs (23 publications) evaluated cohorts of studies approved by RECs; the proportion of published studies had a PI between 22% and 72% and the weighted pooled proportion when combining estimates would be 46.2% (95% CI 40.2%-52.4%, I2 = 94.4%). Twenty-two MRPs (22 publications) evaluated cohorts of studies included in trial registries; the PI of the proportion published ranged from 13% to 90% and the weighted pooled proportion would be 54.2% (95% CI 42.0%-65.9%, I2 = 98.9%). REC-approved studies with statistically significant results (compared with those without statistically significant results) were more likely to be published (pooled OR 2.8; 95% CI 2.2-3.5). Phase-III trials were also more likely to be published than phase II trials (pooled OR 2.0; 95% CI 1.6-2.5). The probability of publication within two years after study completion ranged from 7% to 30%. CONCLUSIONS: A substantial part of the studies approved by RECs or included in trial registries remains unpublished. Due to the large heterogeneity a prediction of the publication probability for a future study is very uncertain. Non-publication of research is not a random process, e.g., it is associated with the direction of study findings. Our findings suggest that the dissemination of research findings is biased

    Chondroprotection by urocortin involves blockade of the mechanosensitive ion channel Piezo1

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    Osteoarthritis (OA) is characterised by progressive destruction of articular cartilage and chondrocyte cell death. Here, we show the expression of the endogenous peptide urocortin1 (Ucn1) and two receptor subtypes, CRF-R1 and CRF-R2, in primary human articular chondrocytes (AC) and demonstrate its role as an autocrine/paracrine pro-survival factor. This effect could only be removed using the CRF-R1 selective antagonist CP-154526, suggesting Ucn1 acts through CRF-R1 when promoting chondrocyte survival. This cell death was characterised by an increase in p53 expression, and cleavage of caspase 9 and 3. Antagonism of CRF-R1 with CP-154526 caused an accumulation of intracellular calcium (Ca2+) over time and cell death. These effects could be prevented with the non-selective cation channel blocker Gadolinium (Gd3+). Therefore, opening of a non-selective cation channel causes cell death and Ucn1 maintains this channel in a closed conformation. This channel was identified to be the mechanosensitive channel Piezo1. We go on to determine that this channel inhibition by Ucn1 is mediated initially by an increase in cyclic adenosine monophosphate (cAMP) and a subsequent inactivation of phospholipase A2 (PLA2), whose metabolites are known to modulate ion channels. Knowledge of these novel pathways may present opportunities for interventions that could abrogate the progression of OA

    Approximate Bayesian inference for analysis of spatio-temporal flood frequency data

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    This is the final version. Available from the Institute of Mathematical Statistics via the DOI in this record. Extreme floods cause casualties, and widespread damage to property and vital civil infrastructure. We here propose a Bayesian approach for predicting extreme floods using the generalized extreme-value (GEV) distribution within gauged and ungauged catchments. A major methodological challenge is to find a suitable parametrization for the GEV distribution when covariates or latent spatial effects are involved. Other challenges involve balancing model complexity and parsimony using an appropriate model selection procedure, and making inference using a reliable and computationally efficient approach. Our approach relies on a latent Gaussian modeling framework with a novel multivariate link function designed to separate the interpretation of the parameters at the latent level and to avoid unreasonable estimates of the shape and time trend parameters. Structured additive regression models are proposed for the four parameters at the latent level. For computational efficiency with large datasets and richly parametrized models, we exploit an accurate and fast approximate Bayesian inference approach. We applied our proposed methodology to annual peak river flow data from 554 catchments across the United Kingdom (UK). Our model performed well in terms of flood predictions for both gauged and ungauged catchments. The results show that the spatial model components for the transformed location and scale parameters, and the time trend, are all important. Posterior estimates of the time trend parameters correspond to an average increase of about 1.5%1.5\% per decade and reveal a spatial structure across the UK. To estimate return levels for spatial aggregates, we further develop a novel copula-based post-processing approach of posterior predictive samples, in order to mitigate the effect of the conditional independence assumption at the data level, and we show that our approach provides accurate results.University of Iceland Research Fun
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