A deformability-based biochip for precise label-free stratification of metastatic subtypes using deep learning.

ABSTRACT: Cellular deformability is a promising biomarker for evaluating the physiological state of cells in medical applications. Microfluidics has emerged as a powerful technique for measuring cellular deformability. However, existing microfluidic-based assays for measuring cellular deformability rely heavily on image analysis, which can limit their scalability for high-throughput applications. Here, we develop a parallel constriction-based microfluidic flow cytometry device and an integrated computational framework (ATMQcD). The ATMQcD framework includes automatic training set generation, multiple object tracking, segmentation, and cellular deformability quantification. The system was validated using cancer cell lines of varying metastatic potential, achieving a classification accuracy of 92.4% f

SUBMITTER: Hua H 

PROVIDER: S-EPMC10539402 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature