Flow mediated dilation of the brachial artery: an investigation of methods requiring further standardization

BACKGROUND: In order to establish a consistent method for brachial artery reactivity assessment, we analyzed commonly used approaches to the test and their effects on the magnitude and time-course of flow mediated dilation (FMD), and on test variability and repeatability. As a popular and noninvasive assessment of endothelial function, several different approaches have been employed to measure brachial artery reactivity with B-mode ultrasound. Despite some efforts, there remains a lack of defined normal values and large variability in measurement technique. METHODS: Twenty-six healthy volunteers underwent repeated brachial artery diameter measurements by B-mode ultrasound. Following baseline diameter recordings we assessed endothelium-dependent flow mediated dilation by inflating a blood pressure cuff either on the upper arm (proximal) or on the forearm (distal). RESULTS: Thirty-seven measures were performed using proximal occlusion and 25 with distal occlusion. Following proximal occlusion relative to distal occlusion, FMD was larger (16.2 ± 1.2% vs. 7.3 ± 0.9%, p < 0.0001) and elongated (107.2 s vs. 67.8 s, p = 0.0001). Measurement of the test repeatability showed that differences between the repeated measures were greater on average when the measurements were done using the proximal method as compared to the distal method (2.4%; 95% CI 0.5–4.3; p = 0.013). CONCLUSION: These findings suggest that forearm compression holds statistical advantages over upper arm compression. Added to documented physiological and practical reasons, we propose that future studies should use forearm compression in the assessment of endothelial function

Structures of enveloped virions determined by cryogenic electron microscopy and tomography : Advances in Virus Research

Enveloped viruses enclose their genomes inside a lipid bilayer which is decorated by membrane proteins that mediate virus entry. These viruses display a wide range of sizes, morphologies and symmetries. Spherical viruses are often isometric and their envelope proteins follow icosahedral symmetry. Filamentous and pleomorphic viruses lack such global symmetry but their surface proteins may display locally ordered assemblies. Determining the structures of enveloped viruses, including the envelope proteins and their protein-protein interactions on the viral surface, is of paramount importance. These structures can reveal how the virions are assembled and released by budding from the infected host cell, how the progeny virions infect new cells by membrane fusion, and how antibodies bind surface epitopes to block infection. In this chapter, we discuss the uses of cryogenic electron microscopy (cryo-EM) in elucidating structures of enveloped virions. Starting from a detailed outline of data collection and processing strategies, we highlight how cryo-EM has been successfully utilized to provide unique insights into enveloped virus entry, assembly, and neutralization.Peer reviewe

Defining family business: a closer look at definitional heterogeneity

Researchers have used a myriad of different definitions in seeking to explain the heterogeneity of family firms and their unique behavior; however, no widely-accepted definition exists today. Definitional clarity in any field is essential to provide (a) the basis for the analysis of performance both spatially and temporally and (b) the foundation upon which theories, frameworks and models are developed. We provide a comprehensive analysis of prior research and identify and classify 82 definitions of family business. We then review and evaluate five key theoretical perspectives in family business to identify how these have shaped and informed the definitions employed in the field and duly explain family firm heterogeneity. Finally, we provide a conceptual diagram to inform the choice of definition in different research settings

Deep learning enables automated scoring of liver fibrosis stages

Current liver fibrosis scoring by computer-assisted image analytics is not fully automated as it requires manual preprocessing (segmentation and feature extraction) typically based on domain knowledge in liver pathology. Deep learning-based algorithms can potentially classify these images without the need for preprocessing through learning from a large dataset of images. We investigated the performance of classification models built using a deep learning-based algorithm pre-trained using multiple sources of images to score liver fibrosis and compared them against conventional non-deep learning-based algorithms - artificial neural networks (ANN), multinomial logistic regression (MLR), support vector machines (SVM) and random forests (RF). Automated feature classification and fibrosis scoring were achieved by using a transfer learning-based deep learning network, AlexNet-Convolutional Neural Networks (CNN), with balanced area under receiver operating characteristic (AUROC) values of up to 0.85–0.95 versus ANN (AUROC of up to 0.87–1.00), MLR (AUROC of up to 0.73–1.00), SVM (AUROC of up to 0.69–0.99) and RF (AUROC of up to 0.94–0.99). Results indicate that a deep learning-based algorithm with transfer learning enables the construction of a fully automated and accurate prediction model for scoring liver fibrosis stages that is comparable to other conventional non-deep learning-based algorithms that are not fully automated