Evidence map›Paper›PMID 34663821›Full record

ArticleNature communications2021

A hierarchical cellular structural model to unravel the universal power-law rheological behavior of living cells.

Jiu-Tao Hang, Yu Kang, Guang-Kui Xu, Huajian Gao

Abstract read
In one paragraph

Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Article
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  3. High-Throughput Nanorheology of Living Cells Powered by Supervised Machine Learning.Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) · 2025
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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

4 authors.

Jiu-Tao HangLaboratory for Multiscale Mechanics and Medical Science, Department of Engineering Mechanics, SVL, School of Aerospace Engineering, Xi'an Jiaotong University, 710049, Xi'an, China.
Yu KangCollege of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China.ORCID 0000-0002-0999-8802
Guang-Kui XuLaboratory for Multiscale Mechanics and Medical Science, Department of Engineering Mechanics, SVL, School of Aerospace Engineering, Xi'an Jiaotong University, 710049, Xi'an, China. guangkuixu@mail.xjtu.edu.cn.ORCID 0000-0001-6393-5434
Huajian GaoSchool of Mechanical and Aerospace Engineering, College of Engineering, Nanyang Technological University, 639798, Singapore, Singapore. huajian.gao@ntu.edu.sg.ORCID 0000-0002-8656-846X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Living cells are a complex soft material with fascinating mechanical properties. A striking feature is that, regardless of their types or states, cells exhibit a universal power-law rheological behavior which to this date still has not been captured by a single theoretical model. Here, we propose a cellular structural model that accounts for the essential mechanical responses of cell membrane, cytoplasm and cytoskeleton. We demonstrate that this model can naturally reproduce the universal power-law characteristics of cell rheology, as well as how its power-law exponent is related to cellular stiffness. More importantly, the power-law exponent can be quantitatively tuned in the range of 0.1 ~ 0.5, as found in most types of cells, by varying the stiffness or architecture of the cytoskeleton. Based on the structural characteristics, we further develop a self-similar hierarchical model that can spontaneously capture the power-law characteristics of creep compliance over time and complex modulus over frequency. The present model suggests that mechanical responses of cells may depend primarily on their generic architectural mechanism, rather than specific molecular properties.

Indexed as

Models, StructuralRheologyCell MembraneCytoplasmCytoskeletonModels, Theoretical

Identifiers

PMID34663821
PMCPMC8523554

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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.