Rheumatic heart disease and endomyocardial fibrosis: Distinguishing the etiology of mitral regurgitation in low-resourced areas

Rheumatic heart disease (RHD) and endomyocardial fibrosis (EMF) are 2 neglected cardiovascular diseases that disproportionately affect young populations, living in poverty. RHD characteristically occurs in low- and middle-income countries, as well as in some disadvantaged populations within high-income countries, such as the Aboriginal peoples of Australia. In contrast, EMF is primarily a tropical cardiomyopathy, with both high-prevalence countries and high-prevalence regions within affected countries.The etiology, pathogenesis, echocardiographic findings, interventions and prognosis are quite distinct. While RHD is unarguably the most preventable of all cardiac diseases, resulting from untreated or undertreated group A streptococcal infections, EMF’s etiology remains unclear. It has been related to infections, dietary factors and toxic agents, and currently there are no specific drugs to treat EMF.The distinction of mitral lesions due to RHD from leftsided EMF, can be difficult in endemic areas for both diseases, especially in the context of lack of resources for diagnosis. However, the correct distinction is highly important since medical management, surgical and interventional options and prognosis are considerably different. Here we describe the features that allow this distinction in African settings where both diseases occur, paying particular emphasis to echocardiography

Advice to health professionals: Use of lignocaine as a diluent to reduce the pain associated with the administration of benzathine penicillin G

No Abstract

A unified analysis of evolutionary and population constraint in protein domains highlights structural features and pathogenic sites

Protein evolution is constrained by structure and function, creating patterns in residue conservation that are routinely exploited to predict structure and other features. Similar constraints should affect variation across individuals, but it is only with the growth of human population sequencing that this has been tested at scale. Now, human population constraint has established applications in pathogenicity prediction, but it has not yet been explored for structural inference. Here, we map 2.4 million population variants to 5885 protein families and quantify residue-level constraint with a new Missense Enrichment Score (MES). Analysis of 61,214 structures from the PDB spanning 3661 families shows that missense depleted sites are enriched in buried residues or those involved in small-molecule or protein binding. MES is complementary to evolutionary conservation and a combined analysis allows a new classification of residues according to a conservation plane. This approach finds functional residues that are evolutionarily diverse, which can be related to specificity, as well as family-wide conserved sites that are critical for folding or function. We also find a possible contrast between lethal and non-lethal pathogenic sites, and a surprising clinical variant hot spot at a subset of missense enriched positions

JABAWS 2.2 Distributed Web Services for Bioinformatics:Protein Disorder, Conservation and RNA Secondary Structure

Abstract Summary JABAWS 2.2 is a computational framework that simplifies the deployment of web services for Bioinformatics. In addition to the five multiple sequence alignment (MSA) algorithms in JABAWS 1.0, JABAWS 2.2 includes three additional MSA programs (Clustal Omega, MSAprobs, GLprobs), four protein disorder prediction methods (DisEMBL, IUPred, Ronn, GlobPlot), 18 measures of protein conservation as implemented in AACon, and RNA secondary structure prediction by the RNAalifold program. JABAWS 2.2 can be deployed on a variety of in-house or hosted systems. JABAWS 2.2 web services may be accessed from the Jalview multiple sequence analysis workbench (Version 2.8 and later), as well as directly via the JABAWS command line interface (CLI) client. JABAWS 2.2 can be deployed on a local virtual server as a Virtual Appliance (VA) or simply as a Web Application Archive (WAR) for private use. Improvements in JABAWS 2.2 also include simplified installation and a range of utility tools for usage statistics collection, and web services querying and monitoring. The JABAWS CLI client has been updated to support all the new services and allow integration of JABAWS 2.2 services into conventional scripts. A public JABAWS 2 server has been in production since December 2011 and served over 800 000 analyses for users worldwide. Availability and implementation JABAWS 2.2 is made freely available under the Apache 2 license and can be obtained from: http://www.compbio.dundee.ac.uk/jabaws. </jats:sec

The at risk child clinic (ARCC): 3 years of health activities in support of the most vulnerable children in Beira, Mozambique

The concept of “children at risk” changes worldwide according to each specific context. Africa has a large burden of overall risk factors related to childhood health and development, most of which are of an infective or social origin. The aim of this study was to report and analyze the volumes of activities of fifteen At Risk Child Clinics (ARCCs) within the Beira District (Mozambique) over a 3 year-period in order to define the health profile of children accessing such health services. We retrospectively analyzed the data from all of the children accessing one of the 15 Beira ARCCs from January 2015 to December 2017. From this, 17,657 first consultations were registered. The motivations for accessing the services were in order of relevance: HIV exposure (n. 12,300; 69.7%), other risk conditions (n. 2542; 14.4%), Moderate Acute Malnutrition (MAM) (n. 1664; 9.4%), Severe Acute Malnutrition (SAM) (n. 772; 4.4%), and TB exposure (n. 542; 3.1%). During the first consultations, 16,865 children were screened for HIV (95.5%), and 7.89% tested HIV-positive. In our three years of experience, HIV exposure was the main indication for children to access the ARCCs in Mozambique. ARCCs could represent a strategic point to better understand health demands and to monitor the quality of care provided to this vulnerable population group, however significant effort is needed to improve the quality of the data collection

PDBe-KB: a community-driven resource for structural and functional annotations.

The Protein Data Bank in Europe-Knowledge Base (PDBe-KB, https://pdbe-kb.org) is a community-driven, collaborative resource for literature-derived, manually curated and computationally predicted structural and functional annotations of macromolecular structure data, contained in the Protein Data Bank (PDB). The goal of PDBe-KB is two-fold: (i) to increase the visibility and reduce the fragmentation of annotations contributed by specialist data resources, and to make these data more findable, accessible, interoperable and reusable (FAIR) and (ii) to place macromolecular structure data in their biological context, thus facilitating their use by the broader scientific community in fundamental and applied research. Here, we describe the guidelines of this collaborative effort, the current status of contributed data, and the PDBe-KB infrastructure, which includes the data exchange format, the deposition system for added value annotations, the distributable database containing the assembled data, and programmatic access endpoints. We also describe a series of novel web-pages-the PDBe-KB aggregated views of structure data-which combine information on macromolecular structures from many PDB entries. We have recently released the first set of pages in this series, which provide an overview of available structural and functional information for a protein of interest, referenced by a UniProtKB accession