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Control of Whole Heart Geometry by Intramyocardial Mechano-Feedback: A Model Study

Overview of attention for article published in PLoS Computational Biology, February 2012
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Title
Control of Whole Heart Geometry by Intramyocardial Mechano-Feedback: A Model Study
Published in
PLoS Computational Biology, February 2012
DOI 10.1371/journal.pcbi.1002369
Pubmed ID
Authors

Theo Arts, Joost Lumens, Wilco Kroon, Tammo Delhaas

Abstract

Geometry of the heart adapts to mechanical load, imposed by pressures and volumes of the cavities. We regarded preservation of cardiac geometry as a homeostatic control system. The control loop was simulated by a chain of models, starting with geometry of the cardiac walls, sequentially simulating circulation hemodynamics, myofiber stress and strain in the walls, transfer of mechano-sensed signals to structural changes of the myocardium, and finalized by calculation of resulting changes in cardiac wall geometry. Instead of modeling detailed mechano-transductive pathways and their interconnections, we used principles of control theory to find optimal transfer functions, representing the overall biological responses to mechanical signals. As biological responses we regarded tissue mass, extent of contractile myocyte structure and extent of the extra-cellular matrix. Mechano-structural stimulus-response characteristics were considered to be the same for atrial and ventricular tissue. Simulation of adaptation to self-generated hemodynamic load rendered physiologic geometry of all cardiac cavities automatically. Adaptation of geometry to chronic hypertension and volume load appeared also physiologic. Different combinations of mechano-sensors satisfied the condition that control of geometry is stable. Thus, we expect that for various species, evolution may have selected different solutions for mechano-adaptation.

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The data shown below were compiled from readership statistics for 88 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Spain 1 1%
United States 1 1%
France 1 1%
Unknown 84 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 26%
Researcher 23 26%
Student > Doctoral Student 6 7%
Student > Bachelor 5 6%
Student > Master 5 6%
Other 13 15%
Unknown 13 15%
Readers by discipline Count As %
Engineering 29 33%
Medicine and Dentistry 11 13%
Agricultural and Biological Sciences 9 10%
Computer Science 5 6%
Biochemistry, Genetics and Molecular Biology 5 6%
Other 15 17%
Unknown 14 16%