A Molecular Communication Detection Method for the Deformability of Erythrocyte Membrane in Blood Vessels

Molecular communication (MC) inspired drug delivery holds considerable promise as a new design for targeted therapy with high efficiency and minimal toxicity. The process of drug delivery can be modelled in a blood flow-based MC system, where nanoparticles (NPs) carry therapeutic agents through the blood vessel channels to the targeted diseased tissue. Most previous studies in the flow-based MC consider a Newtonian fluid with a laminar flow, which ignores the influence of red blood cells (RBCs). However, the nature of blood flow is a complex and non-Newtonian fluid composed of proteins, platelets, plasma and deformable cells, especially RBCs. The ability to change their shapes is essential to the proper functioning of RBCs in the microvasculature. Different shapes of RBCs have a great impact on the performance of blood flow. Changes in the properties and shapes of RBCs are often associated with different diseases, such as sickle cell anemia, diabetes, and malaria. Thus, it is highly important to establish a more realistic blood flow MC model considering the deformable cells. According to our previous study, the motion and adhesion of individual NPs are modelled through the Brownian adhesion dynamics. Subsequently, this paper establishes a particle-cell hybrid model in the flow-based MC, which focuses on the RBC deformation, aggregation, and dispersion in the blood suspension. Based on the state of the RBC deformation and aggregation in the vessels with different flow rates, this paper proposes a novel methodology for detecting the deformability of the cells. The blood state in terms of RBC deformability is determined by the difference in NPs’ concentration at the receiving end., this paper sheds some light on the influence of RBCs on the motion of NPs, which provides new insights on the design of targeted drug delivery and the detection of vascular diseases.