Integrating deep learning with microfluidics for biophysical classification of sickle red blood cells adhered to laminin.

ABSTRACT: Sickle cell disease, a genetic disorder affecting a sizeable global demographic, manifests in sickle red blood cells (sRBCs) with altered shape and biomechanics. sRBCs show heightened adhesive interactions with inflamed endothelium, triggering painful vascular occlusion events. Numerous studies employ microfluidic-assay-based monitoring tools to quantify characteristics of adhered sRBCs from high resolution channel images. The current image analysis workflow relies on detailed morphological characterization and cell counting by a specially trained worker. This is time and labor intensive, and prone to user bias artifacts. Here we establish a morphology based classification scheme to identify two naturally arising sRBC subpopulations-deformable and non-deformable sRBCs-utilizing novel visua

SUBMITTER: Praljak N 

PROVIDER: S-EPMC8659663 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature