Label-free liquid biopsy through the identification of tumor cells by machine learning-powered tomographic phase imaging flow cytometry
Abstract Image-based identification of circulating tumor cells in microfluidic cytometry condition is one of the most challenging perspectives in the Liquid Biopsy scenario. Here we show a machine learning-powered tomographic phase imaging flow cytometry system capable to provide high-throughput 3D...
Main Authors: | Daniele Pirone, Annalaura Montella, Daniele G. Sirico, Martina Mugnano, Massimiliano M. Villone, Vittorio Bianco, Lisa Miccio, Anna Maria Porcelli, Ivana Kurelac, Mario Capasso, Achille Iolascon, Pier Luca Maffettone, Pasquale Memmolo, Pietro Ferraro |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2023-04-01
|
Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-023-32110-9 |
Similar Items
-
Phenotyping neuroblastoma cells through intelligent scrutiny of stain-free biomarkers in holographic flow cytometry
by: Daniele Pirone, et al.
Published: (2023-09-01) -
Loss Minimized Data Reduction in Single-Cell Tomographic Phase Microscopy Using 3D Zernike Descriptors
by: Pasquale Memmolo, et al.
Published: (2023-01-01) -
Dehydration of plant cells shoves nuclei rotation allowing for 3D phase-contrast tomography
by: Zhe Wang, et al.
Published: (2021-09-01) -
Microfluidic engineering for continuous in-flow cyto-tomography
by: Memmolo Pasquale, et al.
Published: (2019-01-01) -
3D imaging lipidometry in single cell by in-flow holographic tomography
by: Daniele Pirone, et al.
Published: (2023-01-01)