Evidence map›Paper›PMID 31820588›Full record

ArticleInternational journal for numerical methods in biomedical engineering2020

Microstructural analysis of skeletal muscle force generation during aging.

Yantao Zhang, Jiun-Shyan Chen, Qizhi He, Xiaolong He, Ramya R Basava, John Hodgson, Usha Sinha, Shantanu Sinha

Abstract read
In one paragraph

Article in International journal for numerical methods in biomedical engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

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  9. The Effects of Aging on the Intramuscular Connective Tissue.International journal of molecular sciences · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Yantao ZhangDepartment of Structural Engineering, University of California San Diego, La Jolla, California, USA.
Jiun-Shyan ChenDepartment of Structural Engineering, University of California San Diego, La Jolla, California, USA.ORCID 0000-0002-6871-8815
Qizhi HeDepartment of Structural Engineering, University of California San Diego, La Jolla, California, USA.
Xiaolong HeDepartment of Structural Engineering, University of California San Diego, La Jolla, California, USA.
Ramya R BasavaDepartment of Structural Engineering, University of California San Diego, La Jolla, California, USA.
John HodgsonDepartment of Integrative Biology and Physiology, University of California Los Angeles, Los Angeles, California, USA.
Usha SinhaDepartment of Physics, San Diego State University, San Diego, California, USA.
Shantanu SinhaDepartment of Radiology, University of California San Diego, La Jolla, California, USA.

Funding

Role of the Extracellular Matrix in Age-Associated Strength Loss: Combining Imaging and Biochemistry to create a Multi-Scale Mesh-free ModelR01AG056999 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SINHA, SHANTANU · 2018 to 2022
$3.1M
National Institute of Health 1R01AG056999-01A1NIA NIH HHS R01 AG056999
6 · The paper itself

Abstract

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. NOVELTY STATEMENT: A cellular-scale honeycomb-like microstructural muscle model constructed from a histological cross-sectional image of muscle is employed to study the causal relations between age-associated microstructural changes and age-related force loss using Reproducing Kernel Particle Method (RKPM). The employed RKPM offers an effective means for modeling biological materials based on pixel points in the medical images and allow modeling of smooth transition in the material properties across interfaces. The proposed microstructure-informed muscle model enables quantitative evaluation on how cellular-scale compositions contribute to muscle functionality and explain differences in age-related force changes during concentric, isometric and eccentric contractions.

Indexed as

Models, BiologicalAdultAgedAgingBiomechanical PhenomenaConnective TissueHumansImaging, Three-DimensionalMuscle, SkeletalNumerical Analysis, Computer-AssistedStress, Mechanicalagingconnective tissueforce generationmicrostructurenumerical simulationreproducing kernel particle methodskeletal muscle

Identifiers

PMID31820588
PMCPMC8080883

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.