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Computational modeling of the obstructive lung diseases asthma and COPD

Overview of attention for article published in Journal of Translational Medicine, November 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

Mentioned by

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1 policy source
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1 X user

Citations

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51 Dimensions

Readers on

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91 Mendeley
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Title
Computational modeling of the obstructive lung diseases asthma and COPD
Published in
Journal of Translational Medicine, November 2014
DOI 10.1186/1479-5876-12-s2-s5
Pubmed ID
Authors

Kelly Suzanne Burrowes, Tom Doel, Chris Brightling

Abstract

Asthma and chronic obstructive pulmonary disease (COPD) are characterized by airway obstruction and airflow limitation and pose a huge burden to society. These obstructive lung diseases impact the lung physiology across multiple biological scales. Environmental stimuli are introduced via inhalation at the organ scale, and consequently impact upon the tissue, cellular and sub-cellular scale by triggering signaling pathways. These changes are propagated upwards to the organ level again and vice versa. In order to understand the pathophysiology behind these diseases we need to integrate and understand changes occurring across these scales and this is the driving force for multiscale computational modeling.

X Demographics

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 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 1%
India 1 1%
Unknown 89 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 15%
Researcher 14 15%
Student > Master 12 13%
Student > Bachelor 10 11%
Professor 5 5%
Other 17 19%
Unknown 19 21%
Readers by discipline Count As %
Engineering 28 31%
Medicine and Dentistry 17 19%
Agricultural and Biological Sciences 4 4%
Computer Science 4 4%
Biochemistry, Genetics and Molecular Biology 2 2%
Other 12 13%
Unknown 24 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 17 September 2020.
All research outputs
#7,355,485
of 25,371,288 outputs
Outputs from Journal of Translational Medicine
#1,225
of 4,634 outputs
Outputs of similar age
#94,426
of 369,437 outputs
Outputs of similar age from Journal of Translational Medicine
#24
of 85 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 4,634 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one has gotten more attention than average, scoring higher than 72% 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 369,437 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 73% of its contemporaries.
We're also able to compare this research output to 85 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 69% of its contemporaries.