Exosome Structures Supported by Machine Learning Can Be Used as a Promising Diagnostic Tool.

ABSTRACT: Principal component analysis (PCA) as a machine-learning technique could serve in disease diagnosis and prognosis by evaluating the dynamic morphological features of exosomes via Cryo-TEM-imaging. This hypothesis was investigated after the crude isolation of similarly featured exosomes derived from the extracellular vehicles (EVs) of immature dendritic cells (IDCs) JAWSII. It is possible to identify functional molecular groups by FTIR, but the unique physical and morphological characteristics of exosomes can only be revealed by specialized imaging techniques such as cryo-TEM. On the other hand, PCA has the ability to examine the morphological features of each of these IDC-derived exosomes by considering software parameters such as various membrane projections and differences in Gaussians,

SUBMITTER: Cansever Mutlu E 

PROVIDER: S-EPMC9693854 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature