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• W4313366893 abstract "The small intestine is the primary site of enzymatic digestion and nutrient absorption in humans. Intestinal contractions facilitate digesta transport, mixing, and contact with the absorptive surfaces. Previous computational models have been limited to idealized contraction patterns and/or simplified geometries to study digesta transport. This study develops a physiologically realistic model of flow and mixing in the first segment of the small intestine (duodenum) based upon a geometry obtained from the Visible Human Project dataset and contraction patterns derived from electrophysiological simulations of slow wave propagation. Features seen in previous simpler models, such as reversed flow underneath the contracting region, were also present in this model for water, Newtonian liquid digesta, and non-Newtonian (power law) whole digesta. An increase in the contraction amplitude from 10% to 50% resulted in faster transport with mean speeds over a cycle increasing from 1.7 to 8.7 mm/s. Glucose transport was advection dominated with Peclet numbers greater than 104. A metric of glucose mixing was computed, with 0 representing no mixing and 1 representing perfect mixing. For antegrade contractions at a 50% amplitude, the metric after 60 s was 0.99 for water, 0.6 for liquid digesta, and 0.19 for whole digesta. Retrograde contractions had a negligible impact on the flow and mixing. Colliding wavefronts resulted in swirling flows and increased the mixing metric by up to 2.6 times relative to antegrade slow wave patterns. The computational framework developed in this study provides new tools for understanding the mixing and nutrient absorption patterns under normal and diseased conditions." @default.
• W4313366893 created "2023-01-06" @default.
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• W4313366893 date "2023-01-01" @default.
• W4313366893 modified "2023-10-02" @default.
• W4313366893 title "Anatomically realistic computational model of flow and mixing in the human duodenum" @default.
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• W4313366893 doi "https://doi.org/10.1063/5.0135070" @default.
• W4313366893 hasPublicationYear "2023" @default.
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