Microstructural analysis of skeletal muscle force generation during aging.
Microstructural analysis of skeletal muscle force generation during aging.
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DOI:
10.1002/cnm.3295
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发表时间:
2020-01
影响因子:
2.1
通讯作者:
Sinha S
中科院分区:
文献类型:
--
作者:
Zhang Y;Chen JS;He Q;He X;Basava RR;Hodgson J;Sinha U;Sinha S
Human aging results in a progressive decline in the active force generation capability of skeletal muscle. While many factors related to the changes of morphological and structural properties in muscle fibers and the extracellular matrix (ECM) have been considered as possible reasons for causing age-related force reduction, it is still not fully understood why the decrease in force generation under eccentric contraction (lengthening) is much less than that under concentric contraction (shortening). Biomechanically, it was observed that connective tissues (endomysium) stiffen as ages, and the volume ratio of connective tissues exhibits an age-related increase. However, limited skeletal muscle models take into account the microstructural characteristics as well as the volume fraction of tissue material. This study aims to provide a numerical investigation in which the muscle fibers and the ECM are explicitly represented to allow quantitative assessment of the age-related force reduction mechanism. To this end, a fiber-level honeycomb-like microstructure is constructed and modeled by a pixel-based Reproducing Kernel Particle Method (RKPM), which allows modeling of smooth transition in biomaterial properties across material interfaces. The numerical investigation reveals that the increased stiffness of the passive materials of muscle tissue reduces the force generation capability under concentric contraction while maintains the force generation capability under eccentric contraction. The proposed RKPM microscopic model provides effective means for the cellular-scale numerical investigation of skeletal muscle physiology.
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DOI:
10.1080/21681163.2015.1049712
发表时间:
2016
期刊:
Computer methods in biomechanics and biomedical engineering. Imaging & visualization
影响因子:
--
作者:
Chen JS;Basava RR;Zhang Y;Csapo R;Malis V;Sinha U;Hodgson J;Sinha S
通讯作者:
Sinha S
影响因子:
10.6
作者:
Chan, TF;Vese, LA
通讯作者:
Vese, LA
DOI:
10.1016/s0045-7825(96)01083-3
发表时间:
1996-12-15
影响因子:
7.2
作者:
Chen, JS;Pan, CH;Liu, WK
通讯作者:
Liu, WK
DOI:
10.1080/10255840701771750
发表时间:
2008-01-01
影响因子:
1.6
作者:
Boel, Markus;Reese, Stefanie
通讯作者:
Reese, Stefanie
影响因子:
3.9
作者:
Barber, Lee A.;Barrett, Rod S.;Lichtwark, Glen A.
通讯作者:
Lichtwark, Glen A.