Solute transport in the brain tissue: what are the key biophysical parameters tying in vivo and in vitro studies together?
The mechanisms of solute transport in brain tissues are still under debate. The medical relevance of this topic has put the blood-brain barrier and the mechanisms of solute transport through the brain parenchyma in the spotlight, notably in the context of brain clearance. In the last decade, the cla...
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Veröffentlicht in: | Biomaterials science 2023-05, Vol.11 (10), p.3450-3460 |
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Sprache: | eng |
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Zusammenfassung: | The mechanisms of solute transport in brain tissues are still under debate. The medical relevance of this topic has put the blood-brain barrier and the mechanisms of solute transport through the brain parenchyma in the spotlight, notably in the context of brain clearance. In the last decade, the classical view of pure diffusive flow across the brain parenchyma was tested against the recent proposal of an active, convectional fluid flow model known as the glymphatic model. Experimental studies of brain transport on living humans and animals have temporal and spatial limitations to validate any of these models. Therefore, detailed microscopic observations, mostly
tissue and simplified
brain models with the support from computational models, are necessary to understand transport mechanisms in brain tissues. However, standardization is lacking between these experimental approaches, which tends to limit the generality of conclusions. In this review, we provide an overview of the output and limitations of modern brain solute transport studies to search for key parameters comparable across experimental setups. We emphasize that
models relying on physiological material and reproducing the biophysical setting of the brain, as well as computational/mathematical models constitute powerful solutions to understand the solute transport phenomena inside of the brain tissue. Finally, we suggest the blood-brain barrier permeability and the apparent diffusion coefficient through the brain parenchyma to be robust biophysical parameters for the extraction of cross-model conclusion. |
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ISSN: | 2047-4830 2047-4849 |
DOI: | 10.1039/d3bm00027c |