Open Access
Competition between Red Blood Cells aggregation and break-up: Depletion force due to filamentous viruses vs shear flow
Olivera Korculanin,Tatiana Kochetkova,Pavlik Lettinga +2 more
- 01 Jan 2018
Vol. 2018
3
TL;DR: It is argued that the RBC doublets can only undergo separation during tumbling motion when the angle between the normal of the doublets with the flow direction is within a critical range, and at sufficiently high shear rates, the time spent in the critical range becomes too short, such that the cells continue to tumble without separating.
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Abstract: Human blood is a shear-thinning fluid with a complex response that strongly depends on the red blood cell’s (RBC’s) ability to form aggregates, called rouleaux. Despite numerous investigations, microscopic understanding of the break up of RBC aggregates has not been fully elucidated. Here, we present a study of breaking up aggregates consisting of two RBCs (a doublet) during shear flow. We introduce the filamentous fd bacteriophage as a rod-like depletant agent with a very long-range interaction force, which can be tuned by the rod’s concentration. We visualize the structures while shearing by combining a home-build counter-rotating cone-plate shear cell with microscopy imaging. A diagram of dynamic states for shear rates versus depletant concentration shows regions of different flow responses and separation stages for the RBCs doublets. With increasing interaction forces, the full-contact flow states dominate, such as rolling and tumbling. We argue that the RBC doublets can only undergo separation during tumbling motion when the angle between the normal of the doublets with the flow direction is within a critical range. However, at sufficiently high shear rates, the time spent in the critical range becomes too short, such that the cells continue to tumble without separating.
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Citations
Aggregation and disaggregation of red blood cells: depletion versus bridging
Nicolas F. Moreno,Kirill Korneev,Alexey Semenov,Alper Topuz,Thomas John,Minne Paul Lettinga,Marco Ellero,Christian Wagner,Dmitry A. Fedosov +8 more
Physiology-based parameterization of human blood steady shear rheology via machine learning: a hemostatistics contribution
TL;DR: This study uses machine learning and the largest standardized dataset to improve the parameterization of the Casson model with respect to hematocrit and fibrinogen concentration for healthy individuals and employs machine learning to identify a potential additional factor, the mean corpuscular hemoglobin (MCH), that may affect blood rheology.
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