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High Performance Computing for Drug Discovery and Biomedicine

Overview of attention for book
Attention for Chapter: Cellular Blood Flow Modeling with HemoCell.
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Chapter title
Cellular Blood Flow Modeling with HemoCell.
Book title
High Performance Computing for Drug Discovery and Biomedicine
Published in
Methods in molecular biology, January 2024
DOI 10.1007/978-1-0716-3449-3_16
Pubmed ID
Book ISBNs
978-1-07-163448-6, 978-1-07-163449-3
Authors

Zavodszky, Gabor, Spieker, Christian, Czaja, Benjamin, van Rooij, Britt

Abstract

Many of the intriguing properties of blood originate from its cellular nature. Bulk effects, such as viscosity, depend on the local shear rates and on the size of the vessels. While empirical descriptions of bulk rheology are available for decades, their validity is limited to the experimental conditions they were observed under. These are typically artificial scenarios (e.g., perfectly straight glass tube or in pure shear with no gradients). Such conditions make experimental measurements simpler; however, they do not exist in real systems (i.e., in a real human circulatory system). Therefore, as we strive to increase our understanding on the cardiovascular system and improve the accuracy of our computational predictions, we need to incorporate a more comprehensive description of the cellular nature of blood. This, however, presents several computational challenges that can only be addressed by high performance computing. In this chapter, we describe HemoCell ( https://www.hemocell.eu ), an open-source high-performance cellular blood flow simulation, which implements validated mechanical models for red blood cells and is capable of reproducing the emergent transport characteristics of such a complex cellular system. We discuss the accuracy and the range of validity, and demonstrate applications on a series of human diseases.

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 33%
Unspecified 2 22%
Researcher 1 11%
Unknown 3 33%
Readers by discipline Count As %
Engineering 3 33%
Unspecified 2 22%
Unknown 4 44%
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 14 September 2023.
All research outputs
#16,597,003
of 24,417,958 outputs
Outputs from Methods in molecular biology
#5,736
of 13,777 outputs
Outputs of similar age
#2,641
of 4,817 outputs
Outputs of similar age from Methods in molecular biology
#4
of 11 outputs
Altmetric has tracked 24,417,958 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,777 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 42nd percentile – i.e., 42% 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 4,817 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.