Evidence map›Paper›PMID 42223638›Full record

ReviewResults and problems in cell differentiation2026

Network-Associated Mechanotransduction in Health and Disease.

Béla Suki, Joseph K Hall, Erzsébet Bartolák-Suki

Abstract readReview
PubMed Publisher
In one paragraph

Review in Results and problems in cell differentiation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Béla SukiDepartment of Biomedical Engineering, Boston University, Boston, MA, USA. bsuki@bu.edu.
Joseph K HallDepartment of Biomedical Engineering, Boston University, Boston, MA, USA.
Erzsébet Bartolák-SukiDepartment of Biomedical Engineering, Boston University, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mechanical forces are known to influence many cellular and extracellular processes in the body via mechanical signal transduction pathways, collectively called mechanotransduction. The corresponding mechanical information transmission is a multiscale process: starting at the nanoscale level of motor proteins, mechanical forces percolate through the cytoskeleton of the cell to create contractile forces in the extracellular matrix, where they are further transmitted to reach the tissue and organ levels. This force transmission is supported by various networks of simpler elements, such as collagen and elastin molecules that form fibrils, fibers, and fiber networks. The efficiency of force transmission, including the magnitude and direction of the force, heavily depends on the organization of the network structure. In the limit, when the transmission pathway through a network is broken, the force signal does not reach its target, and no response can happen. Thus, mechanical force and stress transmission require a connected or percolating network. This chapter reviews how emergent network phenomena contribute to mechanotransduction at various length scales. Sufficiently realistic intra- and extracellular network models should eventually be able to help understand disease pathogenesis and progression, as well as drug effects, with potential for guiding clinical treatments.

Indexed as

CytoskeletonExtracellular MatrixMechanotransduction, CellularAnimalsHumansFibersFilamentsMechanosensitivityNetwork effectsPercolation

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.