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Toward the modeling of mucus draining from human lung: role of airways deformation on air-mucus interaction

Overview of attention for article published in Frontiers in Physiology, August 2015
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Title
Toward the modeling of mucus draining from human lung: role of airways deformation on air-mucus interaction
Published in
Frontiers in Physiology, August 2015
DOI 10.3389/fphys.2015.00214
Pubmed ID
Authors

Benjamin Mauroy, Patrice Flaud, Dominique Pelca, Christian Fausser, Jacques Merckx, Barrett R. Mitchell

Abstract

Chest physiotherapy is an empirical technique used to help secretions to get out of the lung whenever stagnation occurs. Although commonly used, little is known about the inner mechanisms of chest physiotherapy and controversies about its use are coming out regularly. Thus, a scientific validation of chest physiotherapy is needed to evaluate its effects on secretions. We setup a quasi-static numerical model of chest physiotherapy based on thorax and lung physiology and on their respective biophysics. We modeled the lung with an idealized deformable symmetric bifurcating tree. Bronchi and their inner fluids mechanics are assumed axisymmetric. Static data from the literature is used to build a model for the lung's mechanics. Secretions motion is the consequence of the shear constraints apply by the air flow. The input of the model is the pressure on the chest wall at each time, and the output is the bronchi geometry and air and secretions properties. In the limit of our model, we mimicked manual and mechanical chest physiotherapy techniques. We show that for secretions to move, air flow has to be high enough to overcome secretion resistance to motion. Moreover, the higher the pressure or the quicker it is applied, the higher is the air flow and thus the mobilization of secretions. However, pressures too high are efficient up to a point where airways compressions prevents air flow to increase any further. Generally, the first effects of manipulations is a decrease of the airway tree hydrodynamic resistance, thus improving ventilation even if secretions do not get out of the lungs. Also, some secretions might be pushed deeper into the lungs; this effect is stronger for high pressures and for mechanical chest physiotherapy. Finally, we propose and tested two a dimensional numbers that depend on lung properties and that allow to measure the efficiency and comfort of a manipulation.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Unknown 47 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 25%
Researcher 6 13%
Other 3 6%
Professor > Associate Professor 3 6%
Student > Master 3 6%
Other 8 17%
Unknown 13 27%
Readers by discipline Count As %
Engineering 10 21%
Nursing and Health Professions 5 10%
Physics and Astronomy 4 8%
Biochemistry, Genetics and Molecular Biology 3 6%
Mathematics 3 6%
Other 11 23%
Unknown 12 25%
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 25 August 2015.
All research outputs
#17,766,929
of 22,821,814 outputs
Outputs from Frontiers in Physiology
#7,140
of 13,598 outputs
Outputs of similar age
#177,597
of 264,147 outputs
Outputs of similar age from Frontiers in Physiology
#37
of 73 outputs
Altmetric has tracked 22,821,814 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,598 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one is in the 40th percentile – i.e., 40% 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 264,147 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 73 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.