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Edward G. Lakatta

Researcher at National Institutes of Health

Publications -  902
Citations -  95504

Edward G. Lakatta is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Blood pressure & Population. The author has an hindex of 146, co-authored 858 publications receiving 88637 citations. Previous affiliations of Edward G. Lakatta include University of Pittsburgh & University College London.

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Ca2+ and Membrane Potential Transitions During Action Potentials Are Self-Similar to Each Other and to Variability of AP Firing Intervals Across the Broad Physiologic Range of AP Intervals During Autonomic Receptor Stimulation.

TL;DR: In this paper, the authors show that Ca2+ and V m domain kinetic transitions (time to AP ignition in diastole and 90% AP recovery) occurring within given AP, the mean APFIs, and APFI variabilities within the time series of APs in 230 individual nodal cells are self-similar (obey power laws).

Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits

Evangelos Evangelou, +279 more
TL;DR: In this paper, the authors report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry, identifying 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures.
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The Sustained Release of Galardin and Taxol from Gelatin Chondroitin Sulfate Coacervate Films

TL;DR: There is no effective strategy for preventing restenosis in man and percutaneous transluminal balloon angioplasty remains the limiting factor in the use of this treatment for coronary artery disease.
Posted ContentDOI

Discovering patterns of pleiotropy in genome-wide association studies

J Zhana, +131 more
- 28 Feb 2018 - 
TL;DR: Two methods based on a Bayesian framework, SNP And Pleiotropic PHenotype Organization (SAPPHO) and the other incorporating a full phenotype covariance structure are introduced, which learn patterns of pleiotropy from genotype and phenotype data, using identified associations to discover additional associations with shared patterns.
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Computer algorithms for automated detection and analysis of local Ca2+ releases in spontaneously beating cardiac pacemaker cells.

TL;DR: In this paper, the authors measured local Ca2+ releases (LCRs) in spontaneously contracting sinoatrial (SA) node cells isolated from rabbit and guinea pig and developed a new algorithm capable of detecting and analyzing the LCRs spatially in two-dimensions, and in time.