scholarly journals Characterization of Repolarization Alternans in the Coronary Artery Disease

10.5772/28766 ◽  
2012 ◽  
Author(s):  
Laura Burattini ◽  
Roberto Burattini
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Artery Disease

Characterization of cholesteryl ester-loaded human coronary vascular smooth muscle cell secretome. A source of potential biomarkers of coronary artery disease

2021 ◽  
Vol 331 ◽  
pp. e67-e68
Author(s):  
J.E.G. Rodríguez ◽  
A. Benitez Amaro ◽  
L. Claudi ◽  
J. Ni ◽  
C. Pallara ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Smooth Muscle ◽  
Coronary Artery ◽  
Vascular Smooth Muscle ◽  
Cholesteryl Ester ◽  
Smooth Muscle Cell ◽  
Muscle Cell ◽  
Artery Disease ◽  
Potential Biomarkers

Characterization of arbutamine: A novel catecholamine stress agent for diagnosis of coronary artery disease

1994 ◽  
Vol 32 (1) ◽  
pp. 19-28 ◽  
Author(s):  
Mark Young ◽  
Wei Pan ◽  
Jim Wiesner ◽  
David Bullough ◽  
Gene Browne ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Stress Agent ◽  
Artery Disease

Characterization of the warm-up phenomenon in patients with coronary artery disease

2002 ◽  
Vol 144 (5) ◽  
pp. 870-876 ◽  
Author(s):  
Petri Tuomainen ◽  
Esko Vanninen ◽  
Pirjo Halonen ◽  
Keijo Peuhkurinen
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Warm Up ◽  
Artery Disease

scholarly journals Characterization of TCF21 Downstream Target Regions Identifies a Transcriptional Network Linking Multiple Independent Coronary Artery Disease Loci

PLoS Genetics ◽  
2015 ◽  
Vol 11 (5) ◽  
pp. e1005202 ◽  
Author(s):  
Olga Sazonova ◽  
Yuqi Zhao ◽  
Sylvia Nürnberg ◽  
Clint Miller ◽  
Milos Pjanic ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Transcriptional Network ◽  
Downstream Target ◽  
Disease Loci ◽  
Artery Disease

scholarly journals Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants.

2021 ◽  
Author(s):  
Krishna G Aragam ◽  
Tao Jiang ◽  
Anuj Goel ◽  
Stavroula Kanoni ◽  
Brooke N Wolford ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Genome Wide Association Study ◽  
Risk Variants ◽  
A Genome ◽  
Causal Genes ◽  
Causal Variants ◽  
Artery Disease ◽  
Credible Set

Rapid progress of the discovery of genetic loci associated with common, complex diseases has outpaced the elucidation of mechanisms pertinent to disease pathogenesis. To address relevant barriers for coronary artery disease (CAD), we combined genetic discovery analyses with downstream characterization of likely causal variants, genes, and biological pathways. Specifically, we conducted a genome-wide association study (GWAS) comprising 181,522 cases of CAD among 1,165,690 participants. We detected 241 associations, including 54 associations and 30 loci not previously linked to CAD. Next, we prioritized likely causal variants using functionally-informed fine-mapping, yielding 42 associations with fewer than five variants in the 95% credible set. Combining eight complementary predictors, we prioritized 185 candidate causal genes, including 94 genes supported by three or more predictors. Similarity-based clustering underscored a role for early developmental processes, cell cycle signaling, and vascular proliferation in the pathogenesis of CAD. Our analysis identifies and systematically characterizes risk loci for CAD to inform experimental interrogation of putative causal mechanisms for CAD.

Automated characterization of coronary artery disease, myocardial infarction, and congestive heart failure using contourlet and shearlet transforms of electrocardiogram signal

2017 ◽  
Vol 132 ◽  
pp. 156-166 ◽  
Author(s):  
U Rajendra Acharya ◽  
Hamido Fujita ◽  
Vidya K Sudarshan ◽  
Shu Lih Oh ◽  
Muhammad Adam ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Heart Failure ◽  
Coronary Artery Disease ◽  
Congestive Heart Failure ◽  
Coronary Artery ◽  
Electrocardiogram Signal ◽  
Artery Disease
Sign in / Sign up

Export Citation Format

Share Document