Biological Water Dynamics and Entropy: A Biophysical Origin of Cancer and Other Diseases
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Davidson-2013-Biological Water Dyn.pdf
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708.33 KB
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Author(s)
Davidson, Robert M.
Lauritzen, Ann
Seneff, Stephanie
Date Issued
September 2013
Journal
Entropy
Publisher
MDPI AG
Citation
Davidson, Robert, Ann Lauritzen, and Stephanie Seneff. “Biological Water Dynamics and Entropy: A Biophysical Origin of Cancer and Other Diseases.” Entropy 15, no. 9 (September 13, 2013): 3822-3876.
Version
Final published version
Abstract
This paper postulates that water structure is altered by biomolecules as well as by disease-enabling entities such as certain solvated ions, and in turn water dynamics and structure affect the function of biomolecular interactions. Although the structural and dynamical alterations are subtle, they perturb a well-balanced system sufficiently to facilitate disease. We propose that the disruption of water dynamics between and within cells underlies many disease conditions. We survey recent advances in magnetobiology, nanobiology, and colloid and interface science that point compellingly to the crucial role played by the unique physical properties of quantum coherent nanomolecular clusters of magnetized water in enabling life at the cellular level by solving the “problems” of thermal diffusion, intracellular crowding, and molecular self-assembly. Interphase water and cellular surface tension, normally maintained by biological sulfates at membrane surfaces, are compromised by exogenous interfacial water stressors such as cationic aluminum, with consequences that include greater local water hydrophobicity, increased water tension, and interphase stretching. The ultimate result is greater “stiffness” in the extracellular matrix and either the “soft” cancerous state or the “soft” neurodegenerative state within cells. Our hypothesis provides a basis for understanding why so many idiopathic diseases of today are highly stereotyped and pluricausal.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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DOI of Published Version
https://doi.org/10.3390/e15093822
