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Predictive Virtual Infection Modeling of Fungal Immune Evasion in Human Whole Blood

Overview of attention for article published in Frontiers in immunology, March 2018
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  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

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Title
Predictive Virtual Infection Modeling of Fungal Immune Evasion in Human Whole Blood
Published in
Frontiers in immunology, March 2018
DOI 10.3389/fimmu.2018.00560
Pubmed ID
Authors

Maria T. E. Prauße, Teresa Lehnert, Sandra Timme, Kerstin Hünniger, Ines Leonhardt, Oliver Kurzai, Marc Thilo Figge

Abstract

Bloodstream infections by the human-pathogenic fungiCandida albicansandCandida glabrataincreasingly occur in hospitalized patients and are associated with high mortality rates. The early immune response against these fungi in human blood comprises a concerted action of humoral and cellular components of the innate immune system. Upon entering the blood, the majority of fungal cells will be eliminated by innate immune cells, i.e., neutrophils and monocytes. However, recent studies identified a population of fungal cells that can evade the immune response and thereby may disseminate and cause organ dissemination, which is frequently observed during candidemia. In this study, we investigate the so far unresolved mechanism of fungal immune evasion in human whole blood by testing hypotheses with the help of mathematical modeling. We use a previously established state-based virtual infection model for whole-blood infection withC. albicansto quantify the immune response and identified the fungal immune-evasion mechanism. While this process was assumed to be spontaneous in the previous model, we now hypothesize that the immune-evasion process is mediated by host factors and incorporate such a mechanism in the model. In particular, we propose, based on previous studies that the fungal immune-evasion mechanism could possibly arise through modification of the fungal surface by as of yet unknown proteins that are assumed to be secreted by activated neutrophils. To validate or reject any of the immune-evasion mechanisms, we compared the simulation of both immune-evasion models for different infection scenarios, i.e., infection of whole blood with eitherC. albicansorC. glabrataunder non-neutropenic and neutropenic conditions. We found that under non-neutropenic conditions, both immune-evasion models fit the experimental data from whole-blood infection withC. albicansandC. glabrata. However, differences between the immune-evasion models could be observed for the infection outcome under neutropenic conditions with respect to the distribution of fungal cells across the immune cells. Based on these predictions, we suggested specific experimental studies that might allow for the validation or rejection of the proposed immune-evasion mechanism.

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The data shown below were collected from the profiles of 6 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 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 19%
Researcher 5 16%
Student > Postgraduate 4 13%
Student > Doctoral Student 3 9%
Student > Bachelor 3 9%
Other 5 16%
Unknown 6 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 19%
Immunology and Microbiology 6 19%
Biochemistry, Genetics and Molecular Biology 3 9%
Medicine and Dentistry 2 6%
Environmental Science 1 3%
Other 3 9%
Unknown 11 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 09 April 2018.
All research outputs
#7,121,912
of 25,382,440 outputs
Outputs from Frontiers in immunology
#7,902
of 31,537 outputs
Outputs of similar age
#117,919
of 347,622 outputs
Outputs of similar age from Frontiers in immunology
#254
of 694 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 31,537 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.4. This one has gotten more attention than average, scoring higher than 74% 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 347,622 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 65% of its contemporaries.
We're also able to compare this research output to 694 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 63% of its contemporaries.