Aluminum-Induced Entropy in Biological Systems: Implications for Neurological Disease
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Author(s)
Shaw, Christopher A.
Seneff, Stephanie
Kette, Stephen D.
Tomljenovic, Lucija
Oller, John W.
Davidson, Robert M.
Date Issued
October 2014
Journal
Journal of Toxicology
Publisher
Hindawi Publishing Corporation
Citation
Shaw, Christopher A.; Seneff, Stephanie; Kette, Stephen D.; Tomljenovic, Lucija; Oller, John W. and Davidson, Robert M. "Aluminum-Induced Entropy in Biological Systems: Implications for Neurological Disease." Journal of Toxicology 2014, 491316 (October 2014): 1-27 © 2014 Christopher A. Shaw et al
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Final published version
Abstract
Over the last 200 years, mining, smelting, and refining of aluminum (Al) in various forms have increasingly exposed living species to this naturally abundant metal. Because of its prevalence in the earth’s crust, prior to its recent uses it was regarded as inert and therefore harmless. However, Al is invariably toxic to living systems and has no known beneficial role in any biological systems. Humans are increasingly exposed to Al from food, water, medicinals, vaccines, and cosmetics, as well as from industrial occupational exposure. Al disrupts biological self-ordering, energy transduction, and signaling systems, thus increasing biosemiotic entropy. Beginning with the biophysics of water, disruption progresses through the macromolecules that are crucial to living processes (DNAs, RNAs, proteoglycans, and proteins). It injures cells, circuits, and subsystems and can cause catastrophic failures ending in death. Al forms toxic complexes with other elements, such as fluorine, and interacts negatively with mercury, lead, and glyphosate. Al negatively impacts the central nervous system in all species that have been studied, including humans. Because of the global impacts of Al on water dynamics and biosemiotic systems, CNS disorders in humans are sensitive indicators of the Al toxicants to which we are being exposed.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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DOI of Published Version
https://doi.org/10.1155/2014/491316
