137 research outputs found

    Changes in suspected adverse drug reaction reporting via the Yellow Card scheme in Wales following the introduction of a National Reporting Indicator

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    AIMS: This study aimed to assess the impact of a National Reporting Indicator (NRI) on rates of reporting of suspected adverse drug reactions using the Yellow Card scheme following the introduction of the NRI in Wales (UK) in April 2014. METHODS: Yellow Card reporting data for general practitioners and other reporting groups in Wales and England for the financial years 2014–15 (study period 1) and 2015–16 (study period 2) were obtained from the Medicines and Healthcare Products Regulatory Agency and compared with those for 2013–14 (pre‐NRI control period). RESULTS: The numbers of Yellow Cards submitted by general practitioners in Wales were 271, 665 and 870 in the control period, study period 1 and study period 2, respectively. This is equivalent to an increase of 145% in study period 1 and 221% in study period 2 compared with the 12‐month control period (2013–14). Corresponding increases in England were 17% and 37%, respectively (P < .001 chi–squared test). The numbers of Yellow Cards submitted by other groups in Wales were 906, 795 and 947 in each of the study periods. CONCLUSIONS: Introduction of the NRI corresponded with a significant increase in the number of Yellow Cards submitted by general practitioners in Wales. General practitioner reporting rates continued to increase year on year through to 2018–19 with the NRI still in place. No concomitant change was found in reporting rates by other groups in the health boards in Wales

    Character, Incidence, and Predictors of Knee Pain and Activity after Infrapatellar Intramedullary Nailing of an Isolated Tibia Fracture

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    © Copyright 2015 Wolters Kluwer Health, Inc. All rights reserved. Objective: To study the activity and incidence of knee pain after sustaining an isolated tibia fracture treated with an infrapatellar intramedullary nail at 1 year. Design: Retrospective review of prospective cohort. Setting: Multicenter Academic and Community hospitals. Patients: Four hundred thirty-seven patients with an isolated tibia fracture completed a 12-month assessment on pain and self-reported activity. Intervention: Infrapatellar intramedullary nail. Outcomes: Demographic information, comorbid conditions, injury characteristics, and surgical technique were recorded. Knee pain was defined on a 1-7 scale with 1 being no pain and 7 being a very great deal of pain. Knee pain \u3e4 was considered clinically significant. Patients reported if they were able, able with difficulty, or unable to perform the following activities: kneel, run, climb stairs, and walk prolonged. Variables were tested in multilevel multivariable regression analyses. Results: In knee pain, 11% of patients reported a good deal to a very great deal of pain (\u3e4), and 52% of patients reported no or very little pain at 12 months. In activity at 12 months, 26% and 29% of patients were unable to kneel or run, respectively, and 31% and 35% of patients, respectively, stated they were able with difficulty or unable to use stairs or walk. Conclusions: Clinically significant knee pain (\u3e4/7) was present in 11% of patients 1 year after a tibia fracture. Of note, 31%-71% of patients had difficulty performing or were unable to perform routine daily activities of kneeling, running, and stair climbing, or walking prolonged distances

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

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    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

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    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

    Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

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

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

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    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

    Drug Off-Target Effects Predicted Using Structural Analysis in the Context of a Metabolic Network Model

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    Recent advances in structural bioinformatics have enabled the prediction of protein-drug off-targets based on their ligand binding sites. Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of these two capabilities provides a framework for evaluating metabolic drug response phenotypes in silico. This combined approach was applied to investigate the hypertensive side effect of the cholesteryl ester transfer protein inhibitor torcetrapib in the context of human renal function. A metabolic kidney model was generated in which to simulate drug treatment. Causal drug off-targets were predicted that have previously been observed to impact renal function in gene-deficient patients and may play a role in the adverse side effects observed in clinical trials. Genetic risk factors for drug treatment were also predicted that correspond to both characterized and unknown renal metabolic disorders as well as cryptic genetic deficiencies that are not expected to exhibit a renal disorder phenotype except under drug treatment. This study represents a novel integration of structural and systems biology and a first step towards computational systems medicine. The methodology introduced herein has important implications for drug development and personalized medicine

    The Framingham Heart Study 100K SNP genome-wide association study resource: overview of 17 phenotype working group reports

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    Background: The Framingham Heart Study (FHS), founded in 1948 to examine the epidemiology of cardiovascular disease, is among the most comprehensively characterized multi-generational studies in the world. Many collected phenotypes have substantial genetic contributors; yet most genetic determinants remain to be identified. Using single nucleotide polymorphisms (SNPs) from a 100K genome-wide scan, we examine the associations of common polymorphisms with phenotypic variation in this community-based cohort and provide a full-disclosure, web-based resource of results for future replication studies. Methods: Adult participants (n = 1345) of the largest 310 pedigrees in the FHS, many biologically related, were genotyped with the 100K Affymetrix GeneChip. These genotypes were used to assess their contribution to 987 phenotypes collected in FHS over 56 years of follow up, including: cardiovascular risk factors and biomarkers; subclinical and clinical cardiovascular disease; cancer and longevity traits; and traits in pulmonary, sleep, neurology, renal, and bone domains. We conducted genome-wide variance components linkage and population-based and family-based association tests. Results: The participants were white of European descent and from the FHS Original and Offspring Cohorts (examination 1 Offspring mean age 32 ± 9 years, 54% women). This overview summarizes the methods, selected findings and limitations of the results presented in the accompanying series of 17 manuscripts. The presented association results are based on 70,897 autosomal SNPs meeting the following criteria: minor allele frequency ≥ 10%, genotype call rate ≥ 80%, Hardy-Weinberg equilibrium p-value ≥ 0.001, and satisfying Mendelian consistency. Linkage analyses are based on 11,200 SNPs and short-tandem repeats. Results of phenotype-genotype linkages and associations for all autosomal SNPs are posted on the NCBI dbGaP website at http:// www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. Conclusion: We have created a full-disclosure resource of results, posted on the dbGaP website, from a genome-wide association study in the FHS. Because we used three analytical approaches to examine the association and linkage of 987 phenotypes with thousands of SNPs, our results must be considered hypothesis-generating and need to be replicated. Results from the FHS 100K project with NCBI web posting provides a resource for investigators to identify high priority findings for replication.Molecular and Cellular Biolog

    Procalcitonin Is Not a Reliable Biomarker of Bacterial Coinfection in People With Coronavirus Disease 2019 Undergoing Microbiological Investigation at the Time of Hospital Admission

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    Abstract Admission procalcitonin measurements and microbiology results were available for 1040 hospitalized adults with coronavirus disease 2019 (from 48 902 included in the International Severe Acute Respiratory and Emerging Infections Consortium World Health Organization Clinical Characterisation Protocol UK study). Although procalcitonin was higher in bacterial coinfection, this was neither clinically significant (median [IQR], 0.33 [0.11–1.70] ng/mL vs 0.24 [0.10–0.90] ng/mL) nor diagnostically useful (area under the receiver operating characteristic curve, 0.56 [95% confidence interval, .51–.60]).</jats:p
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