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A Physiologically Based, Multi-Scale Model of Skeletal Muscle Structure and Function

Overview of attention for article published in Frontiers in Physiology, January 2012
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Title
A Physiologically Based, Multi-Scale Model of Skeletal Muscle Structure and Function
Published in
Frontiers in Physiology, January 2012
DOI 10.3389/fphys.2012.00358
Pubmed ID
Authors

O. Röhrle, J. B. Davidson, A. J. Pullan

Abstract

Models of skeletal muscle can be classified as phenomenological or biophysical. Phenomenological models predict the muscle's response to a specified input based on experimental measurements. Prominent phenomenological models are the Hill-type muscle models, which have been incorporated into rigid-body modeling frameworks, and three-dimensional continuum-mechanical models. Biophysically based models attempt to predict the muscle's response as emerging from the underlying physiology of the system. In this contribution, the conventional biophysically based modeling methodology is extended to include several structural and functional characteristics of skeletal muscle. The result is a physiologically based, multi-scale skeletal muscle finite element model that is capable of representing detailed, geometrical descriptions of skeletal muscle fibers and their grouping. Together with a well-established model of motor-unit recruitment, the electro-physiological behavior of single muscle fibers within motor units is computed and linked to a continuum-mechanical constitutive law. The bridging between the cellular level and the organ level has been achieved via a multi-scale constitutive law and homogenization. The effect of homogenization has been investigated by varying the number of embedded skeletal muscle fibers and/or motor units and computing the resulting exerted muscle forces while applying the same excitatory input. All simulations were conducted using an anatomically realistic finite element model of the tibialis anterior muscle. Given the fact that the underlying electro-physiological cellular muscle model is capable of modeling metabolic fatigue effects such as potassium accumulation in the T-tubular space and inorganic phosphate build-up, the proposed framework provides a novel simulation-based way to investigate muscle behavior ranging from motor-unit recruitment to force generation and fatigue.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 148 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 2 1%
Australia 2 1%
Switzerland 1 <1%
Malaysia 1 <1%
Germany 1 <1%
Czechia 1 <1%
Mexico 1 <1%
Spain 1 <1%
Unknown 138 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 33 22%
Student > Master 21 14%
Researcher 17 11%
Student > Bachelor 16 11%
Student > Doctoral Student 9 6%
Other 19 13%
Unknown 33 22%
Readers by discipline Count As %
Engineering 54 36%
Medicine and Dentistry 11 7%
Agricultural and Biological Sciences 10 7%
Sports and Recreations 10 7%
Computer Science 7 5%
Other 18 12%
Unknown 38 26%
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 13 September 2012.
All research outputs
#20,166,700
of 22,678,224 outputs
Outputs from Frontiers in Physiology
#9,270
of 13,468 outputs
Outputs of similar age
#221,187
of 244,101 outputs
Outputs of similar age from Frontiers in Physiology
#208
of 309 outputs
Altmetric has tracked 22,678,224 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,468 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.5. This one is in the 1st percentile – i.e., 1% 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 244,101 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 309 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.