Identification and mapping real-world data sources for heart failure, acute coronary syndrome, and atrial fibrillation

BACKGROUND: Transparent and robust real-world evidence sources are increasingly important for global health, including cardiovascular diseases. We aimed to identify global real-world data (RWD) sources for heart failure (HF), acute coronary syndrome (ACS), and atrial fibrillation (AF). METHODS: We conducted a systematic review of publications with RWD pertaining to HF, ACS, and AF (2010-2018), generating a list of unique data sources. Metadata were extracted based on the source type (e.g. electronic health records, genomics, clinical data), study design, population size, clinical characteristics, follow-up duration, outcomes, and assessment of data availability for future studies and linkage. RESULTS: Overall, 11,889 publications were retrieved for HF, 10,729 for ACS, and 6,262 for AF. From these, 322 (HF), 287 (ACS), and 220 (AF) data sources were selected for detailed review. Majority of data sources had near complete data on demographic variables (HF: 94%, ACS: 99%, and AF: 100%) and considerable data on comorbidities (HF: 77%, ACS: 93%, and AF: 97%). The least reported data categories were drug codes (HF, ACS, and AF: 10%) and caregiver involvement (HF: 6%, ACS: 1%, and AF: 1%). Only a minority of data sources provided information on access to data for other researchers (11%) or whether data could be linked to other data sources to maximize clinical impact (20%). The list and metadata for the RWD sources are publicly available at www.escardio.org/bigdata. CONCLUSIONS: This review has created a comprehensive resource of cardiovascular data sources, providing new avenues to improve future real-world research and to achieve better patient outcomes

Influences of Excluded Volume of Molecules on Signaling Processes on Biomembrane

We investigate the influences of the excluded volume of molecules on biochemical reaction processes on 2-dimensional surfaces using a model of signal transduction processes on biomembranes. We perform simulations of the 2-dimensional cell-based model, which describes the reactions and diffusion of the receptors, signaling proteins, target proteins, and crowders on the cell membrane. The signaling proteins are activated by receptors, and these activated signaling proteins activate target proteins that bind autonomously from the cytoplasm to the membrane, and unbind from the membrane if activated. If the target proteins bind frequently, the volume fraction of molecules on the membrane becomes so large that the excluded volume of the molecules for the reaction and diffusion dynamics cannot be negligible. We find that such excluded volume effects of the molecules induce non-trivial variations of the signal flow, defined as the activation frequency of target proteins, as follows. With an increase in the binding rate of target proteins, the signal flow varies by i) monotonically increasing; ii) increasing then decreasing in a bell-shaped curve; or iii) increasing, decreasing, then increasing in an S-shaped curve. We further demonstrate that the excluded volume of molecules influences the hierarchical molecular distributions throughout the reaction processes. In particular, when the system exhibits a large signal flow, the signaling proteins tend to surround the receptors to form receptor-signaling protein clusters, and the target proteins tend to become distributed around such clusters. To explain these phenomena, we analyze the stochastic model of the local motions of molecules around the receptor.Comment: 31 pages, 10 figure

Predicting a small molecule-kinase interaction map: A machine learning approach

<p>Abstract</p> <p>Background</p> <p>We present a machine learning approach to the problem of protein ligand interaction prediction. We focus on a set of binding data obtained from 113 different protein kinases and 20 inhibitors. It was attained through ATP site-dependent binding competition assays and constitutes the first available dataset of this kind. We extract information about the investigated molecules from various data sources to obtain an informative set of features.</p> <p>Results</p> <p>A Support Vector Machine (SVM) as well as a decision tree algorithm (C5/See5) is used to learn models based on the available features which in turn can be used for the classification of new kinase-inhibitor pair test instances. We evaluate our approach using different feature sets and parameter settings for the employed classifiers. Moreover, the paper introduces a new way of evaluating predictions in such a setting, where different amounts of information about the binding partners can be assumed to be available for training. Results on an external test set are also provided.</p> <p>Conclusions</p> <p>In most of the cases, the presented approach clearly outperforms the baseline methods used for comparison. Experimental results indicate that the applied machine learning methods are able to detect a signal in the data and predict binding affinity to some extent. For SVMs, the binding prediction can be improved significantly by using features that describe the active site of a kinase. For C5, besides diversity in the feature set, alignment scores of conserved regions turned out to be very useful.</p

Hyperstable U1snRNA complementary to the K-ras transcripts induces cell death in pancreatic cancer cells

One of the critical steps that governs the inhibitory effect of antisense RNA on target gene expression is the association of the antisense RNA with the target RNA molecules. However, until now, no systematic method has been available to select the suitable parts of a gene as antisense targets. In this study, we utilised U1 small nuclear RNA (snRNA) that binds physiologically to the 5′ splice site (5′ss) of pre-mRNA, to develop a novel vector system that permits imposed binding of antisense RNA to its target. The 5′ free end of U1snRNA was replaced with the antisense sequence against the K-ras gene to generate a hyperstable U1snRNA, whose binding stability to 5′ss of the K-ras transcript is ten-fold higher than that of wild-type U1snRNA. The efficacy of such hyperstable U1snRNA was examined by transducing the expression plasmids into human pancreatic cancer cell lines. This revealed that two of the hyperstable U1snRNAs induced cell death after gene transduction, and significantly reduced the number of G418-resistant colonies to less than 10% of the controls. Furthermore, hyperstable U1snRNA suppressed intraperitoneal dissemination of pancreatic cancer cells in vivo. Hyperstable U1snRNA might be a novel approach to express effective antisense RNA in target cells

Induction of protective and therapeutic anti-pancreatic cancer immunity using a reconstructed MUC1 DNA vaccine

<p>Abstract</p> <p>Background</p> <p>Pancreatic cancer is a common, highly lethal disease with a rising incidence. MUC1 is a tumor-associated antigen that is over-expressed in pancreatic adenocarcinoma. Active immunotherapy that targets MUC1 could have great treatment value. Here we investigated the preventive and therapeutic effect of a MUC1 DNA vaccine on the pancreatic cancer.</p> <p>Methods</p> <p>MUC1-various tandem repeat units(VNTR) DNA vaccine was produced by cloning one repeat of VNTR and inserting the cloned gene into the pcDNA3.1. In the preventive group, female C57BL/6 mice were immunized with the vaccine, pcDNA3.1 or PBS; and challenged with panc02-MUC1 or panc02 cell. In the therapeutic group the mice were challenged with panc02-MUC1 or panc02 cell, and then immunized with the vaccine, pcDNA3.1 or PBS. The tumor size and the survival time of the animals were compared between these groups.</p> <p>Results</p> <p>The DNA vaccine pcDNA3.1-VNTR could raise cytotoxic T lymphocyte (CTL) activity specific for MUC1. In the preventive experiment, the mice survival time was significantly longer in the vaccine group than in the control groups (<it>P </it>< 0.05). In the therapeutic experiment, the DNA vaccine prolonged the survival time of the panc02-MUC1-bearing mice (<it>P </it>< 0.05). In both the preventive and therapeutic experiments, the tumor size was significantly less in the vaccine group than in the control groups (<it>P </it>< 0.05). This pcDNA3.1-VNTR vaccine, however, could not prevent the mice attacked by panc02 cells and had no therapeutic effect on the mice attacked by panc02 cells.</p> <p>Conclusion</p> <p>The MUC1 DNA vaccine pcDNA3.1-VNTR could induce a significant MUC1-specific CTL response; and had both prophylactic and therapeutic effect on panc02-MUC1 tumors. This vaccine might be used as a new adjuvant strategy against pancreatic cancer.</p

Cellular Radiosensitivity: How much better do we understand it?

Purpose: Ionizing radiation exposure gives rise to a variety of lesions in DNA that result in genetic instability and potentially tumorigenesis or cell death. Radiation extends its effects on DNA by direct interaction or by radiolysis of H2O that generates free radicals or aqueous electrons capable of interacting with and causing indirect damage to DNA. While the various lesions arising in DNA after radiation exposure can contribute to the mutagenising effects of this agent, the potentially most damaging lesion is the DNA double strand break (DSB) that contributes to genome instability and/or cell death. Thus in many cases failure to recognise and/or repair this lesion determines the radiosensitivity status of the cell. DNA repair mechanisms including homologous recombination (HR) and non-homologous end-joining (NHEJ) have evolved to protect cells against DNA DSB. Mutations in proteins that constitute these repair pathways are characterised by radiosensitivity and genome instability. Defects in a number of these proteins also give rise to genetic disorders that feature not only genetic instability but also immunodeficiency, cancer predisposition, neurodegeneration and other pathologies. Conclusions: In the past fifty years our understanding of the cellular response to radiation damage has advanced enormously with insight being gained from a wide range of approaches extending from more basic early studies to the sophisticated approaches used today. In this review we discuss our current understanding of the impact of radiation on the cell and the organism gained from the array of past and present studies and attempt to provide an explanation for what it is that determines the response to radiation

Epigenetic regulation of the secreted frizzled-related protein family in human glioblastoma multiforme

Glioblastoma multiforme (GBM) are intracranial tumors of the central nervous system and the most lethal among solid tumors. Current therapy is palliative and is limited to surgical resection followed by radiation therapy and temozolomide treatment. Aberrant WNT pathway activation mediates not only cancer cell proliferation but also promotes radiation and chemotherapeutic resistance. WNT antagonists such as the secreted frizzled-related protein (sFRP) family have an ability to sensitize glioma cells to chemotherapeutics, decrease proliferation rate and induce apoptosis. During tumor development, sFRP genes (1–5) are frequently hypermethylated, causing transcriptional silencing. We investigated a possible involvement of methylation-mediated silencing of the sFRP gene family in human GBM using four human glioblastoma cell lines (U87, U138, A172 and LN18). To induce demethylation of the DNA, we inhibited DNA methyltransferases through treatment with 5-azacytidine. Genomic DNA, RNA and total protein were isolated from GBM cells before and after treatment. We utilized bisulfite modification of genomic DNA to examine the methylation status of the respective sFRP promoter regions. Pharmacological demethylation of the GBM cell lines demonstrated a loss of methylation in sFRP promoter regions, as well as an increase in sFRP gene-specific mRNA abundance. Western blot analysis demonstrated an increased protein expression of sFRP-4 and increased levels of phosphorylated-ß-catenin. These data indicate an important role of methylation-induced gene silencing of the sFRP gene family in human GBM

Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015

SummaryBackground The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. Methods We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors—the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). Findings Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57·8% (95% CI 56·6–58·8) of global deaths and 41·2% (39·8–42·8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211·8 million [192·7 million to 231·1 million] global DALYs), smoking (148·6 million [134·2 million to 163·1 million]), high fasting plasma glucose (143·1 million [125·1 million to 163·5 million]), high BMI (120·1 million [83·8 million to 158·4 million]), childhood undernutrition (113·3 million [103·9 million to 123·4 million]), ambient particulate matter (103·1 million [90·8 million to 115·1 million]), high total cholesterol (88·7 million [74·6 million to 105·7 million]), household air pollution (85·6 million [66·7 million to 106·1 million]), alcohol use (85·0 million [77·2 million to 93·0 million]), and diets high in sodium (83·0 million [49·3 million to 127·5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. Interpretation Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. Funding Bill & Melinda Gates Foundation