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Rossing K.,Copenhagen University | Bosselmann H.S.,Copenhagen University | Gustafsson F.,Copenhagen University | Zhang Z.-Y.,Catholic University of Leuven | And 8 more authors.
PLoS ONE | Year: 2016

Background Biomarker discovery and new insights into the pathophysiology of heart failure with reduced ejection fraction (HFrEF) may emerge from recent advances in high-throughput urinary proteomics. This could lead to improved diagnosis, risk stratification and management of HFrEF. Methods and Results Urine samples were analyzed by on-line capillary electrophoresis coupled to electrospray ionization micro time-of-flight mass spectrometry (CE-MS) to generate individual urinary proteome profiles. In an initial biomarker discovery cohort, analysis of urinary proteome profiles from 33 HFrEF patients and 29 age- and sex-matched individuals without HFrEF resulted in identification of 103 peptides that were significantly differentially excreted in HFrEF. These 103 peptides were used to establish the support vector machine-based HFrEF classifier HFrEF103. In a subsequent validation cohort, HFrEF103 very accurately (area under the curve, AUC = 0.972) discriminated between HFrEF patients (N = 94, sensitivity = 93.6%) and control individuals with and without impaired renal function and hypertension (N = 552, specificity = 92.9%). Interestingly, HFrEF103 showed low sensitivity (12.6%) in individuals with diastolic left ventricular dysfunction (N = 176). The HFrEF-related peptide biomarkers mainly included fragments of fibrillar type I and III collagen but also, e.g., of fibrinogen beta and alpha-1-antitrypsin. Conclusion CE-MS based urine proteome analysis served as a sensitive tool to determine a vast array of HFrEF-related urinary peptide biomarkers which might help improving our understanding and diagnosis of heart failure. © 2016 Rossing et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Source


Jedrychowski W.A.,Jagiellonian University | Perera F.P.,Columbia University | Maugeri U.,Institute for Clinical Medicine | Spengler J.,Harvard University | And 7 more authors.
Cardiovascular Toxicology | Year: 2012

Exposure to fine particulate matter (PM) is a recognized risk factor for elevated blood pressure (BP) and cardiovascular disease in adults, and this prospective cohort study was undertaken to evaluate whether gesta-tional exposure to PM2.5 has a prohypertensive effect. We measured personal exposure to fine particulate matter (PM2.5) by personal air monitoring in the second trimester of pregnancy among 431 women, and BP values in the third trimester were obtained from medical records of prenatal care clinics. In the general estimating equation model, the effect of PM 2.5 on BP was adjusted for relevant covariates such as maternal age, education, parity, gesta-tional weight gain (GWG), prepregnancy BMI, environmental tobacco smoke (ETS), and blood lead level. Systolic blood pressure (SBP) increased in a linear fashion across a dosage of PM2.5 and on average augmented by 6.1 mm Hg (95% CI, 0.6-11.6) with log unit of PM 2.5 concentration. Effects of age, maternal education, prepre-gnancy BMI, blood lead level, and ETS were insignificant. Women with excessive gestational weight gain ([18 kg) had higher mean SBP parameters by 5.5 mmHg (95% CI, 2.7-8.3). In contrast, multiparous women had significantly lower SBP values (coeff. = -4.2 mm Hg; 95% CI, -6.8 to -1.6). Similar analysis performed for diastolic blood pressure (DBP) has demonstrated that PM2.5 also affected DBP parameters (coeff. = 4.1; 95% CI, -0.02 to 8.2), but at the border significance level. DBP values were positively associated with the excessive GWG (coeff. = 2.3; 95% CI, 0.3-4.4) but were inversely related to parity (coeff. = -2.7; 95% CI, -4.6 to -0.73). In the observed cohort, the exposure to fine particulate matter during pregnancy was associated with increased maternal blood pressure. © 2011 Springer Science+Business Media, LLC. Source


Quigley D.,University of California at San Francisco | Quigley D.,University of Oslo | Quigley D.,Institute for Clinical Medicine
Journal of Investigative Dermatology | Year: 2014

Psoriasis is a chronic inflammatory skin disease driven by aberrant signals from the immune system. In this issue, Li et al. present the first large RNA-seq analysis of gene expression in normal skin and psoriasis lesions, providing a more comprehensive view of mRNA expression than earlier microarray studies. This study's size enables gene co-expression analysis, a method illustrating which pathways are altered by the presence of disease. © 2014 The Society for Investigative Dermatology. Source


Unden J.,Institute for Clinical Science | Ingebrigtsen T.,Institute for Clinical Medicine | Romner B.,Institute for Clinical Medicine
BMC Medicine | Year: 2013

Background: The management of minimal, mild and moderate head injuries is still controversial. In 2000, the Scandinavian Neurotrauma Committee (SNC) presented evidence-based guidelines for initial management of these injuries. Since then, considerable new evidence has emerged.Methods: General methodology according to the Appraisal of Guidelines for Research and Evaluation (AGREE) II framework and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. Systematic evidence-based review according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, based upon relevant clinical questions with respect to patient-important outcomes, including Quality Assessment of Diagnostic Accuracy Studies (QUADAS) and Centre of Evidence Based Medicine (CEBM) quality ratings. Based upon the results, GRADE recommendations, guidelines and discharge instructions were drafted. A modified Delphi approach was used for consensus and relevant clinical stakeholders were consulted.Conclusions: We present the updated SNC guidelines for initial management of minimal, mild and moderate head injury in adults including criteria for computed tomography (CT) scan selection, admission and discharge with suggestions for monitoring routines and discharge advice for patients. The guidelines are designed to primarily detect neurosurgical intervention with traumatic CT findings as a secondary goal. For elements lacking good evidence, such as in-hospital monitoring, routines were largely based on consensus. We suggest external validation of the guidelines before widespread clinical use is recommended. © 2013 Undén et al; licensee BioMed Central Ltd. Source


Margolin A.A.,Sage Bionetworks | Bilal E.,IBM | Huang E.,Sage Bionetworks | Huang E.,Duke University | And 40 more authors.
Science Translational Medicine | Year: 2013

Although molecular prognostics in breast cancer are among the most successful examples of translating genomic analysis to clinical applications, optimal approaches to breast cancer clinical risk prediction remain controversial. The Sage Bionetworks-DREAM Breast Cancer Prognosis Challenge (BCC) is a crowdsourced research study for breast cancer prognostic modeling using genome-scale data. The BCC provided a community of data analysts with a common platform for data access and blinded evaluation of model accuracy in predicting breast cancer survival on the basis of gene expression data, copy number data, and clinical covariates. This approach offered the opportunity to assess whether a crowdsourced community Challenge would generate models of breast cancer prognosis commensurate with or exceeding current best-in-class approaches. The BCC comprised multiple rounds of blinded evaluations on held-out portions of data on 1981 patients, resulting in more than 1400 models submitted as open source code. Participants then retrained their models on the full data set of 1981 samples and submitted up to five models for validation in a newly generated data set of 184 breast cancer patients. Analysis of the BCC results suggests that the best-performing modeling strategy outperformed previously reported methods in blinded evaluations; model performance was consistent across several independent evaluations; and aggregating community-developed models achieved performance on par with the best-performing individual models. Copyright 2013 by the American Association for the Advancement of Science; all rights reserved. Source

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