Cold storage surpasses the impact of biological age and donor characteristics on red blood cell morphology classified by deep machine learning.

ABSTRACT: Assessment of the morphology of red blood cells (RBCs) can improve clinical benefits following blood transfusion. Deep machine learning surpasses traditional microscopy-based classification methods, offering more accurate and consistent results while reducing time and labor intensity. RBCs from teenage males, teenage females, senior males, and senior females were biologically age-profiled or density-separated into dense/old RBCs (O-RBCs) and less-dense/young (Y-RBCs) throughout hypothermic storage and assessed using image flow cytometry with deep machine learning analysis. Regardless of biological age, morphology index decreased with hypothermic storage. Significant differences in RBC morphology index were not seen when comparing unseparated RBCs (U-RBCs), O-RBCs, and Y-RBCs, although the

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC11882836 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature