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A Multiscale Agent-Based in silico Model of Liver Fibrosis Progression

Overview of attention for article published in Frontiers in Bioengineering and Biotechnology, May 2014
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  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

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Title
A Multiscale Agent-Based in silico Model of Liver Fibrosis Progression
Published in
Frontiers in Bioengineering and Biotechnology, May 2014
DOI 10.3389/fbioe.2014.00018
Pubmed ID
Authors

Joyeeta Dutta-Moscato, Alexey Solovyev, Qi Mi, Taichiro Nishikawa, Alejandro Soto-Gutierrez, Ira J. Fox, Yoram Vodovotz

Abstract

Chronic hepatic inflammation involves a complex interplay of inflammatory and mechanical influences, ultimately manifesting in a characteristic histopathology of liver fibrosis. We created an agent-based model (ABM) of liver tissue in order to computationally examine the consequence of liver inflammation. Our liver fibrosis ABM (LFABM) is comprised of literature-derived rules describing molecular and histopathological aspects of inflammation and fibrosis in a section of chemically injured liver. Hepatocytes are modeled as agents within hexagonal lobules. Injury triggers an inflammatory reaction, which leads to activation of local Kupffer cells and recruitment of monocytes from circulation. Portal fibroblasts and hepatic stellate cells are activated locally by the products of inflammation. The various agents in the simulation are regulated by above-threshold concentrations of pro- and anti-inflammatory cytokines and damage-associated molecular pattern molecules. The simulation progresses from chronic inflammation to collagen deposition, exhibiting periportal fibrosis followed by bridging fibrosis, and culminating in disruption of the regular lobular structure. The ABM exhibited key histopathological features observed in liver sections from rats treated with carbon tetrachloride (CCl4). An in silico "tension test" for the hepatic lobules predicted an overall increase in tissue stiffness, in line with clinical elastography literature and published studies in CCl4-treated rats. Therapy simulations suggested differential anti-fibrotic effects of neutralizing tumor necrosis factor alpha vs. enhancing M2 Kupffer cells. We conclude that a computational model of liver inflammation on a structural skeleton of physical forces can recapitulate key histopathological and macroscopic properties of CCl4-injured liver. This multiscale approach linking molecular and chemomechanical stimuli enables a model that could be used to gain translationally relevant insights into liver fibrosis.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Netherlands 1 2%
Brazil 1 2%
Unknown 63 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 24%
Student > Ph. D. Student 14 21%
Student > Master 6 9%
Student > Bachelor 5 8%
Student > Postgraduate 3 5%
Other 8 12%
Unknown 14 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 18%
Medicine and Dentistry 10 15%
Engineering 8 12%
Biochemistry, Genetics and Molecular Biology 6 9%
Computer Science 3 5%
Other 12 18%
Unknown 15 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 27 August 2014.
All research outputs
#12,607,342
of 22,756,196 outputs
Outputs from Frontiers in Bioengineering and Biotechnology
#1,319
of 6,524 outputs
Outputs of similar age
#102,251
of 226,629 outputs
Outputs of similar age from Frontiers in Bioengineering and Biotechnology
#7
of 15 outputs
Altmetric has tracked 22,756,196 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,524 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 79% of its peers.
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 226,629 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 15 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 53% of its contemporaries.