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Prediction of acute coronary syndromes by urinary proteome analysis
N.M. Htun
, D.J. Magliano
, Z.Y. Zhang
, J. Lyons
, T. Petit
, E. Nkuipou-Kenfack
, A. Ramirez-Torres
, C. von zur Muhlen
, D. Maahs
, J.P. Schanstra
, C. Pontillo
, M. Pejchinovski
, J.K. Snell-Bergeon
, C. Delles
, H. Mischak
, J.A. Staessen
, J.E. Shaw
, T. Koeck
, K. Peter
*
*
Corresponding author for this work
Epidemiologie
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Keyphrases
Acute Coronary Syndrome
100%
Australia
10%
Biomarker Patterns
10%
C-statistic
30%
Clinical Practice
10%
Composite Algorithm
10%
Coronary Artery Disease
10%
Europe
10%
Framingham Risk Score
20%
Individual Identification
10%
Integrated Discrimination Improvement
10%
Net Reclassification Improvement
10%
North America
10%
Peptide Biomarkers
10%
Plasma Proteomics
30%
Polypeptide Pattern
10%
Preventive Measures
10%
Prognostic Biomarker
20%
Prognostic Value
10%
Proteomic Analysis
100%
Proteomic Biomarkers
10%
Proteomics Data
10%
Risk Factors
10%
Risk Scoring
20%
Underlying Etiologies
10%
Urinary Biomarkers
10%
Urine Proteome
100%
Urine Samples
10%
Validation Cohort
10%
INIS
algorithms
9%
arteries
9%
australia
9%
biological markers
54%
control
18%
coronaries
100%
data
9%
diseases
9%
etiology
9%
europe
9%
north america
9%
patients
9%
peptides
36%
prediction
100%
risks
36%
sampling
9%
statistics
27%
urine
9%
validation
9%
values
9%
Biochemistry, Genetics and Molecular Biology
Prospective Study
25%
Proteomics
100%
Urine Sampling
25%