Cell deformability heterogeneity recognition by unsupervised machine learning from in-flow motion parameters-Reference-Cited by-同舟云学术

Cell deformability heterogeneity recognition by unsupervised machine learning from in-flow motion parameters

Published:2022 Issue:24 Volume:22 Page:4871-4881
ISSN:1473-0197
Container-title:Lab on a Chip
language:en
Short-container-title:Lab Chip

Author:

Maremonti Maria Isabella¹^ORCID,Dannhauser David¹^ORCID,Panzetta Valeria¹^ORCID,Netti Paolo Antonio¹,Causa Filippo¹^ORCID

Affiliation:

1. Interdisciplinary Research Centre on Biomaterials (CRIB) and Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università degli Studi di Napoli “Federico II”, Piazzale Tecchio 80, 80125 Naples, Italy

Abstract

This work demonstrates how a small set of motion parameters uniquely measures a wide range of cell deformability in microfluidics.

Publisher

Royal Society of Chemistry (RSC)

Subject

Biomedical Engineering,General Chemistry,Biochemistry,Bioengineering

Link

http://pubs.rsc.org/en/content/articlepdf/2022/LC/D2LC00902A

Reference55 articles.

1. Biomechanics and biophysics of cancer cells☆

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