Evidence map›Paper›PMID 27786286›Full record

ArticleScientific reports2016

A model for cyclic mechanical reinforcement.

Zhenhai Li, Fang Kong, Cheng Zhu

Abstract read
In one paragraph

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. The Energetic Landscape of Catch Bonds in TCR Interfaces.Journal of immunology (Baltimore, Md. : 1950) · 2023
    Review
  2. Receptor-Ligand Binding: Effect of Mechanical Factors.International journal of molecular sciences · 2023
    Review
  3. Review
  4. Review
  5. Article
  6. Review
  7. Receptor-mediated cell mechanosensing.Molecular biology of the cell · 2017
    Review
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

3 authors.

Zhenhai LiWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Fang KongSingapore MIT Alliance of Research and Technology, Infectious Disease IRG, Singapore 138602, Singapore.
Cheng ZhuWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.

Funding

Visualizing Single Integrin Conformational Changes on Living CellsR01AI044902 · NIAID · GEORGIA INSTITUTE OF TECHNOLOGY · PI ZHU, CHENG · 2000 to 2016
$5.8M
NIAID NIH HHS R01 AI044902
6 · The paper itself

Abstract

Mechanical force regulates a broad range of molecular interactions in biology. Three types of counterintuitive mechanical regulation of receptor-ligand dissociation have been described. Catch bonds are strengthened by constant forces, as opposed to slip bonds that are weakened by constant forces. The phenomenon that bonds become stronger with prior application of cyclic forces is termed cyclic mechanical reinforcement (CMR). Slip and catch bonds have respectively been explained by two-state models. However, they assume fast equilibration between internal states and hence are inadequate for CMR. Here we propose a three-state model for CMR where both loading and unloading regulate the transition of bonds among the short-lived, intermediate, and long-lived state. Cyclic forces favor bonds in the long-lived state, hence greatly prolonging their lifetimes. The three-state model explains the force history effect and agrees with the experimental CMR effect of integrin α

Indexed as

Models, BiologicalBiomechanical PhenomenaCell Adhesion MoleculesEnergy MetabolismExtracellular MatrixFibronectinsIntegrin alpha5beta1Mechanotransduction, CellularProtein ConformationCell Adhesion MoleculesFibronectinsIntegrin alpha5beta1

Identifiers

PMID27786286
PMCPMC5081513

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.