Citations

Echocardiography is the key tool for the diagnosis and evaluation of aortic stenosis. Because clinical decision-making is based on the echocardiographic assessment of its severity, it is essential that standards are adopted to maintain accuracy and consistency across echocardiographic laboratories. Detailed recommendations for the echocardiographic assessment of valve stenosis were published by the European Association of Echocardiography and the American Society of Echocardiography in 2009. In the meantime, numerous new studies on aortic stenosis have been published with particular new insights into the difficult subgroup of low gradient aortic stenosis making an update of recommendations necessary. The document focuses in particular on the optimization of left ventricular outflow tract assessment, low flow, low gradient aortic stenosis with preserved ejection fraction, a new classification of aortic stenosis by gradient, flow and ejection fraction, and a grading algorithm for an integrated and stepwise approach of artic stenosis assessment in clinical practice.

Pancreatitis is an inflammatory disease of the pancreas characterized by dysregulated activity of digestive enzymes, necrosis, immune infiltration, and pain. Repeated incidence of pancreatitis is an important risk factor for pancreatic cancer. Legumain, a lysosomal cysteine protease, has been linked to inflammatory diseases such as atherosclerosis, stroke, and cancer. Until now, legumain activation has not been studied during pancreatitis. We used a fluorescently quenched activity-based probe to assess legumain activation during caerulein-induced pancreatitis in mice. We detected activated legumain by ex vivo imaging, confocal microscopy, and gel electrophoresis. Compared with healthy controls, legumain activity in the pancreas of caerulein-treated mice was increased in a time-dependent manner. Legumain was localized to CD68+ macrophages and was not active in pancreatic acinar cells. Using a small-molecule inhibitor of legumain, we found that this protease is not essential for the initiation of pancreatitis. However, it may serve as a biomarker of disease, since patients with chronic pancreatitis show strongly increased legumain expression in macrophages. Moreover, the occurrence of legumain-expressing macrophages in regions of acinar-to-ductal metaplasia suggests that this protease may influence reprogramming events that lead to inflammation-induced pancreatic cancer.

With the increased availability of computational resources, the past decade has seen a rise in the use of computational fluid dynamics (CFD) for medical applications. There has been an increase in the application of CFD to attempt to predict the rupture of intracranial aneurysms, however, while many hemodynamic parameters can be obtained from these computations, to date, no consistent methodology for the prediction of the rupture has been identified. One particular challenge to CFD is that many factors contribute to its accuracy; the mesh resolution and spatial/temporal discretization can alone contribute to a variation in accuracy. This failure to identify the importance of these factors and identify a methodology for the prediction of ruptures has limited the acceptance of CFD among physicians for rupture prediction. The International CFD Rupture Challenge 2013 seeks to comment on the sensitivity of these various CFD assumptions to predict the rupture by undertaking a comparison of the rupture and blood-flow predictions from a wide range of independent participants utilizing a range of CFD approaches. Twenty-six groups from 15 countries took part in the challenge. Participants were provided with surface models of two intracranial aneurysms and asked to carry out the corresponding hemodynamics simulations, free to choose their own mesh, solver, and temporal discretization. They were requested to submit velocity and pressure predictions along the centerline and on specified planes. The first phase of the challenge, described in a separate paper, was aimed at predicting which of the two aneurysms had previously ruptured and where the rupture site was located. The second phase, described in this paper, aims to assess the variability of the solutions and the sensitivity to the modeling assumptions. Participants were free to choose boundary conditions in the first phase, whereas they were prescribed in the second phase but all other CFD modeling parameters were not prescribed. In order to compare the computational results of one representative group with experimental results, steady-flow measurements using particle image velocimetry (PIV) were carried out in a silicone model of one of the provided aneurysms. Approximately 80% of the participating groups generated similar results. Both velocity and pressure computations were in good agreement with each other for cycle-averaged and peak-systolic predictions. Most apparent “outliers” (results that stand out of the collective) were observed to have underestimated velocity levels compared to the majority of solutions, but nevertheless identified comparable flow structures. In only two cases, the results deviate by over 35% from the mean solution of all the participants. Results of steady CFD simulations of the representative group and PIV experiments were in good agreement. The study demonstrated that while a range of numerical schemes, mesh resolution, and solvers was used, similar flow predictions were observed in the majority of cases. To further validate the computational results, it is suggested that time-dependent measurements should be conducted in the future. However, it is recognized that this study does not include the biological aspects of the aneurysm, which needs to be considered to be able to more precisely identify the specific rupture risk of an intracranial aneurysm.

AbstractIntroductionAfrican‐American (AA) individuals have a higher risk for late‐onset Alzheimer's disease (LOAD) than Americans of primarily European ancestry (EA). Recently, the largest genome‐wide association study in AAs to date confirmed that six of the Alzheimer's disease (AD)‐related genetic variants originally discovered in EA cohorts are also risk variants in AA; however, the risk attributable to many of the loci (e.g., APOE, ABCA7) differed substantially from previous studies in EA. There likely are risk variants of higher frequency in AAs that have not been discovered.MethodsWe performed a comprehensive analysis of genetically determined local and global ancestry in AAs with regard to LOAD status.ResultsCompared to controls, LOAD cases showed higher levels of African ancestry, both globally and at several LOAD relevant loci, which explained risk for AD beyond global differences.DiscussionExploratory post hoc analyses highlight regions with greatest differences in ancestry as potential candidate regions for future genetic analyses.

AbstractBackgroundLate‐onset Alzheimer's disease (AD) is heritable with 20 genes showing genome‐wide association in the International Genomics of Alzheimer's Project (IGAP). To identify the biology underlying the disease, we extended these genetic data in a pathway analysis.MethodsThe ALIGATOR and GSEA algorithms were used in the IGAP data to identify associated functional pathways and correlated gene expression networks in human brain.ResultsALIGATOR identified an excess of curated biological pathways showing enrichment of association. Enriched areas of biology included the immune response (P = 3.27 × 10−12 after multiple testing correction for pathways), regulation of endocytosis (P = 1.31 × 10−11), cholesterol transport (P = 2.96 × 10−9), and proteasome‐ubiquitin activity (P = 1.34 × 10−6). Correlated gene expression analysis identified four significant network modules, all related to the immune response (corrected P = .002–.05).ConclusionsThe immune response, regulation of endocytosis, cholesterol transport, and protein ubiquitination represent prime targets for AD therapeutics.

ObjectiveAggregation of α‐synuclein (α‐syn) and α‐syn cytotoxicity are hallmarks of sporadic and familial Parkinson disease (PD), with accumulating evidence that prefibrillar oligomers and protofibrils are the pathogenic species in PD and related synucleinopathies. Peroxisome proliferator‐activated receptor γ coactivator 1α (PGC‐1α), a key regulator of mitochondrial biogenesis and cellular energy metabolism, has recently been associated with the pathophysiology of PD. Despite extensive effort on studying the function of PGC‐1α in mitochondria, no studies have addressed whether PGC‐1α directly influences oligomerization of α‐syn or whether α‐syn oligomers impact PGC‐1α expression.Materials and MethodsWe tested whether pharmacological or genetic activation of PGC‐1α or PGC‐11α knockdown could modulate the oligomerization of α‐syn in vitro by using an α‐syn ‐fragment complementation assay.ResultsIn this study, we found that both PGC‐1α reference gene (RG‐PGC‐1α) and the central nervous system (CNS)‐specific PGC‐1α (CNS‐PGC‐1α) are downregulated in human PD brain, in A30P α‐syn transgenic animals, and in a cell culture model for α‐syn oligomerization. Importantly, downregulation of both RG‐PGC‐1α and CNS‐PGC‐1α in cell culture or neurons from RG‐PGC‐1α–deficient mice leads to a strong induction of α‐syn oligomerization and toxicity. In contrast, pharmacological activation or genetic overexpression of RG‐PGC‐1α reduced α‐syn oligomerization and rescued α‐syn–mediated toxicity.InterpretationBased on our results, we propose that PGC‐1α downregulation and α‐syn oligomerization form a vicious circle, thereby influencing and/or potentiating each other. Our data indicate that restoration of PGC‐1α is a promising approach for development of effective drugs for the treatment of PD and related synucleinopathies. ANN NEUROL 2015;77:15–32