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The innate immune response to ischemic injury: a multiscale modeling perspective

Overview of attention for article published in BMC Systems Biology, April 2018
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  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

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Title
The innate immune response to ischemic injury: a multiscale modeling perspective
Published in
BMC Systems Biology, April 2018
DOI 10.1186/s12918-018-0580-z
Pubmed ID
Authors

Elena Dimitrova, Leslie A. Caromile, Reinhard Laubenbacher, Linda H. Shapiro

Abstract

Cell death as a result of ischemic injury triggers powerful mechanisms regulated by germline-encoded Pattern Recognition Receptors (PRRs) with shared specificity that recognize invading pathogens and endogenous ligands released from dying cells, and as such are essential to human health. Alternatively, dysregulation of these mechanisms contributes to extreme inflammation, deleterious tissue damage and impaired healing in various diseases. The Toll-like receptors (TLRs) are a prototypical family of PRRs that may be powerful anti-inflammatory targets if agents can be designed that antagonize their harmful effects while preserving host defense functions. This requires an understanding of the complex interactions and consequences of targeting the TLR-mediated pathways as well as technologies to analyze and interpret these, which will then allow the simulation of perturbations targeting specific pathway components, predict potential outcomes and identify safe and effective therapeutic targets. We constructed a multiscale mathematical model that spans the tissue and intracellular scales, and captures the consequences of targeting various regulatory components of injury-induced TLR4 signal transduction on potential pro-inflammatory or pro-healing outcomes. We applied known interactions to simulate how inactivation of specific regulatory nodes affects dynamics in the context of injury and to predict phenotypes of potential therapeutic interventions. We propose rules to link model behavior to qualitative estimates of pro-inflammatory signal activation, macrophage infiltration, production of reactive oxygen species and resolution. We tested the validity of the model by assessing its ability to reproduce published data not used in its construction. These studies will enable us to form a conceptual framework focusing on TLR4-mediated ischemic repair to assess potential molecular targets that can be utilized therapeutically to improve efficacy and safety in treating ischemic/inflammatory injury.

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The data shown below were collected from the profiles of 3 X users 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 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 35%
Student > Doctoral Student 2 9%
Researcher 2 9%
Student > Bachelor 1 4%
Professor 1 4%
Other 4 17%
Unknown 5 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 17%
Biochemistry, Genetics and Molecular Biology 3 13%
Business, Management and Accounting 2 9%
Medicine and Dentistry 2 9%
Mathematics 1 4%
Other 4 17%
Unknown 7 30%
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 31 May 2019.
All research outputs
#15,504,780
of 23,041,514 outputs
Outputs from BMC Systems Biology
#646
of 1,144 outputs
Outputs of similar age
#209,868
of 329,244 outputs
Outputs of similar age from BMC Systems Biology
#17
of 44 outputs
Altmetric has tracked 23,041,514 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 1,144 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 32nd percentile – i.e., 32% 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 329,244 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 44 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 54% of its contemporaries.