Date of Graduation

5-2022

Document Type

Thesis

Degree Name

Bachelor of Science in Biomedical Engineering

Degree Level

Undergraduate

Department

Biomedical Engineering

Advisor/Mentor

Nayani, Karthik

Abstract

The worldwide prevalence of diabetes mellitus is rapidly increasing with about 9.3% of the adult population living with the disease. People with diabetes have trouble regulating their blood glucose levels which typically leads to hyperglycemia. Under normal physiological conditions, erythrocytes can undergo deformations in response to shear stress when passing through capillaries with a smaller diameter. Poorly managed hyperglycemia can lead to the glycosylation of erythrocyte membrane proteins and hemoglobin. This glycosylation leads to increased rigidity of the cells along with decreased deformability in response to mechanical stress; therefore, these cells have a higher susceptibility of getting stuck in the microvasculature leading to a greater chance of cardiovascular complications. Currently, several methods can assess the deformability of red blood cells, but many of the methods don’t utilize a solvent that accurately mimics blood plasma, and they tend to be time-consuming. This study utilizes liquid crystals (LC), specifically disodium cromoglycate (DSCG), which are highly organized molecules that exhibit anisotropy when in the nematic phase. When red blood cells are transferred from a pure aqueous solution to the nematic phase of LC, they exhibit shape changes from a biconcave disk to an elongated oval. The use of DSCG in this study along with microscopy could serve as a novel method for the assessment of erythrocyte deformability when exposed to different glucose concentrations. Results were gathered using ImageJ to extract aspect ratio data from microscopic images. The results were compared between the varying glycemic states (5 mM, 45 mM, 100 mM) to confirm the trend of increased glucose concentrations leading to decreased cell deformability. The data obtained indicated this trend, yet more will need to be collected for this to be considered a viable method. Additionally, the incorporation of machine learning will enable this method to become a novel diagnostic tool for assessing blood glucose concentration.

Keywords

Erythrocytes; Deformability; Glycosylation; Liquid Crystals; Diabetes

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