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Development of a Patient-Specific Multi-Scale Model to Understand Atherosclerosis and Calcification Locations: Comparison with In vivo Data in an Aortic Dissection

Overview of attention for article published in Frontiers in Physiology, June 2016
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  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

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Title
Development of a Patient-Specific Multi-Scale Model to Understand Atherosclerosis and Calcification Locations: Comparison with In vivo Data in an Aortic Dissection
Published in
Frontiers in Physiology, June 2016
DOI 10.3389/fphys.2016.00238
Pubmed ID
Authors

Mona Alimohammadi, Cesar Pichardo-Almarza, Obiekezie Agu, Vanessa Díaz-Zuccarini

Abstract

Vascular calcification results in stiffening of the aorta and is associated with hypertension and atherosclerosis. Atherogenesis is a complex, multifactorial, and systemic process; the result of a number of factors, each operating simultaneously at several spatial and temporal scales. The ability to predict sites of atherogenesis would be of great use to clinicians in order to improve diagnostic and treatment planning. In this paper, we present a mathematical model as a tool to understand why atherosclerotic plaque and calcifications occur in specific locations. This model is then used to analyze vascular calcification and atherosclerotic areas in an aortic dissection patient using a mechanistic, multi-scale modeling approach, coupling patient-specific, fluid-structure interaction simulations with a model of endothelial mechanotransduction. A number of hemodynamic factors based on state-of-the-art literature are used as inputs to the endothelial permeability model, in order to investigate plaque and calcification distributions, which are compared with clinical imaging data. A significantly improved correlation between elevated hydraulic conductivity or volume flux and the presence of calcification and plaques was achieved by using a shear index comprising both mean and oscillatory shear components (HOLMES) and a non-Newtonian viscosity model as inputs, as compared to widely used hemodynamic indicators. The proposed approach shows promise as a predictive tool. The improvements obtained using the combined biomechanical/biochemical modeling approach highlight the benefits of mechanistic modeling as a powerful tool to understand complex phenomena and provides insight into the relative importance of key hemodynamic parameters.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 44 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 32%
Researcher 5 11%
Student > Master 4 9%
Student > Bachelor 3 7%
Student > Doctoral Student 2 5%
Other 2 5%
Unknown 14 32%
Readers by discipline Count As %
Engineering 18 41%
Mathematics 2 5%
Environmental Science 2 5%
Biochemistry, Genetics and Molecular Biology 1 2%
Pharmacology, Toxicology and Pharmaceutical Science 1 2%
Other 4 9%
Unknown 16 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 June 2019.
All research outputs
#15,379,002
of 22,879,161 outputs
Outputs from Frontiers in Physiology
#6,701
of 13,671 outputs
Outputs of similar age
#223,368
of 353,105 outputs
Outputs of similar age from Frontiers in Physiology
#75
of 175 outputs
Altmetric has tracked 22,879,161 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,671 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one is in the 47th percentile – i.e., 47% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 353,105 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 175 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.