Evaluating the impact of post-trial implementation of RHIVA nurse-led HIV screening on HIV testing, diagnosis and earlier diagnosis in general practice in London, UK.

BACKGROUND: UK and European guidelines recommend HIV testing in general practice. We report on the implementation of the Rapid HIV Assessment trial (RHIVA2) promoting HIV screening in general practice into routine care. METHODS: Interrupted time-series, difference-in-difference analysis and Pearson-correlation on three cohorts comprising 42 general practices in City & Hackney (London, UK); covering three periods: pre-trial (2009-2010), trial (2010-2012) and implementation (2012-2014). Cohorts comprised practices receiving: "trial intervention" only (n = 19), "implementation intervention" only (n = 13); and neither ("comparator") (n = 10). Primary outcomes were HIV testing and diagnosis rates per 1000 people and CD4 at diagnosis. FINDINGS: Overall, 55,443 people were tested (including 38,326 among these cohorts), and 101 people were newly diagnosed HIV positive (including 65 among these cohorts) including 74 (73%) heterosexuals and 69 (68%) people of black African/Caribbean background; with mean CD4 count at diagnosis 357 (SD=237). Among implementation intervention practices, testing rate increased by 85% (from 1·798 (95%CI=(1·657,1·938) at baseline to 3·081 (95%CI=(2·865,3·306); p = 0·0000), diagnosis rate increased by 34% (from 0·0026 (95%CI=(0·0004,0·0037)) to 0·0035 (95%CI=(0·0007,0·0062); p = 0·736), and mean CD4 count at diagnosis increased by 55% (from 273 (SD=372) to 425 (SD=274) cells per μL; p = 0·433). Implementation intervention and trial intervention practices achieved similar testing rates (3·764 vs. 3·081; 6% difference; 95% CI=(-5%,18%); p = 0·358), diagnosis rates (0·0035 vs. 0·0081; -13% difference; 95%CI=(-77%,244%; p = 0·837), and mean CD4 count (425 (SD=274) vs. 351 (SD=257); 69% increase; 95% CI=(-61%,249%); p = 0·359). HIV testing was positively correlated with diagnosis (r = 0·114 (95% CI=[0·074,0·163])), and diagnosis with CD4 count at diagnosis (r = 0·011 (95% CI=[-0·177,0·218])). INTERPRETATION: Implementation of the RHIVA programme promoting nurse-led HIV screening into routine practice in inner-city practices with high HIV prevalence increased HIV testing, and may be associated with increased and earlier diagnosis. HIV screening in primary care should be considered a key strategy to reduce undiagnosed infection particularly among high risk persons not attending sexual health services. FUNDING: National Institute for Health Research ARC North Thames, and Barts and The London School of Medicine and Dentistry

Australian shellfish ecosystems: past distribution, current status and future direction

We review the status of marine shellfish ecosystems formed primarily by bivalves in Australia, including: identifying ecosystem-forming species, assessing their historical and current extent, causes for decline and past and present management. Fourteen species of bivalves were identified as developing complex, three-dimensional reef or bed ecosystems in intertidal and subtidal areas across tropical, subtropical and temperate Australia. A dramatic decline in the extent and condition of Australia's two most common shellfish ecosystems, developed by Saccostrea glomerata and Ostrea angasi oysters, occurred during the mid-1800s to early 1900s in concurrence with extensive harvesting for food and lime production, ecosystem modification, disease outbreaks and a decline in water quality. Out of 118 historical locations containing O. angasi-developed ecosystems, only one location still contains the ecosystem whilst only six locations are known to still contain S. glomerata-developed ecosystems out of 60 historical locations. Ecosystems developed by the introduced oyster Crasostrea gigas are likely to be increasing in extent, whilst data on the remaining 11 ecosystem-forming species are limited, preventing a detailed assessment of their current ecosystem-forming status. Our analysis identifies that current knowledge on extent, physical characteristics, biodiversity and ecosystem services of Australian shellfish ecosystems is extremely limited. Despite the limited information on shellfish ecosystems, a number of restoration projects have recently been initiated across Australia and we propose a number of existing government policies and conservation mechanisms, if enacted, would readily serve to support the future conservation and recovery of Australia's shellfish ecosystems

Integrated Analyses of microRNAs Demonstrate Their Widespread Influence on Gene Expression in High-Grade Serous Ovarian Carcinoma

The Cancer Genome Atlas (TCGA) Network recently comprehensively catalogued the molecular aberrations in 487 high-grade serous ovarian cancers, with much remaining to be elucidated regarding the microRNAs (miRNAs). Here, using TCGA ovarian data, we surveyed the miRNAs, in the context of their predicted gene targets.Integration of miRNA and gene patterns yielded evidence that proximal pairs of miRNAs are processed from polycistronic primary transcripts, and that intronic miRNAs and their host gene mRNAs derive from common transcripts. Patterns of miRNA expression revealed multiple tumor subtypes and a set of 34 miRNAs predictive of overall patient survival. In a global analysis, miRNA:mRNA pairs anti-correlated in expression across tumors showed a higher frequency of in silico predicted target sites in the mRNA 3'-untranslated region (with less frequency observed for coding sequence and 5'-untranslated regions). The miR-29 family and predicted target genes were among the most strongly anti-correlated miRNA:mRNA pairs; over-expression of miR-29a in vitro repressed several anti-correlated genes (including DNMT3A and DNMT3B) and substantially decreased ovarian cancer cell viability.This study establishes miRNAs as having a widespread impact on gene expression programs in ovarian cancer, further strengthening our understanding of miRNA biology as it applies to human cancer. As with gene transcripts, miRNAs exhibit high diversity reflecting the genomic heterogeneity within a clinically homogeneous disease population. Putative miRNA:mRNA interactions, as identified using integrative analysis, can be validated. TCGA data are a valuable resource for the identification of novel tumor suppressive miRNAs in ovarian as well as other cancers

Energy Saving on Campus: A comparison of students' attitudes and reported behaviours in the UK and Portugal

Energy saving on campus is an increasingly important part of universities’ responses to climate change, but can only be fully realised through a partnership between institutions and students. This study explores similarities and differences between students’ energy-related attitudes and reported behaviours, as well as their perceptions of their institution’s energy saving efforts using data from two universities in the United Kingdom (UK) and one in Portugal. The results indicate that there are differences between the students’ responses at the selected universities which appear to reflect the national context and diverse institutional priorities. Key differences include the variation between students’ perceptions of individual agency and their university’s environmental practices (stronger in the UK) and students’ sense of collective agency and trust in the government and business (stronger in Portugal). The study is the first to attempt a comparison between students from institutions in different countries in relation to energy saving. It provides a foundation to extend the comparison to other institutions and other countries, and to expand the research to encompass actual energy use, in relation to perceived energy use

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Graphical models for inferring single molecule dynamics

<p>Abstract</p> <p>Background</p> <p>The recent explosion of experimental techniques in single molecule biophysics has generated a variety of novel time series data requiring equally novel computational tools for analysis and inference. This article describes in general terms how graphical modeling may be used to learn from biophysical time series data using the variational Bayesian expectation maximization algorithm (VBEM). The discussion is illustrated by the example of single-molecule fluorescence resonance energy transfer (smFRET)<it> versus</it> time data, where the smFRET time series is modeled as a hidden Markov model (HMM) with Gaussian observables. A detailed description of smFRET is provided as well.</p> <p>Results</p> <p>The VBEM algorithm returns the model’s evidence and an approximating posterior parameter distribution given the data. The former provides a metric for model selection via maximum evidence (ME), and the latter a description of the model’s parameters learned from the data. ME/VBEM provide several advantages over the more commonly used approach of maximum likelihood (ML) optimized by the expectation maximization (EM) algorithm, the most important being a natural form of model selection and a well-posed (non-divergent) optimization problem.</p> <p>Conclusions</p> <p>The results demonstrate the utility of graphical modeling for inference of dynamic processes in single molecule biophysics.</p