ReviewResults and problems in cell differentiation2026
Network-Associated Mechanotransduction in Health and Disease.
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.
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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.
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Authors and funding
3 authors.
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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.
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Registered trials
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.