Label-free cell classification in holographic flow cytometry through an unbiased learning strategy-Reference-Cited by-同舟云学术

Label-free cell classification in holographic flow cytometry through an unbiased learning strategy

Published:2024 Issue: Volume: Page:
ISSN:1473-0197
Container-title:Lab on a Chip
language:en
Short-container-title:Lab Chip

Author:

Ciaparrone Gioele¹,Pirone Daniele²,Fiore Pierpaolo¹^ORCID,Xin Lu³,Xiao Wen³,Li Xiaoping⁴,Bardozzo Francesco¹²,Bianco Vittorio²^ORCID,Miccio Lisa²,Pan Feng³^ORCID,Memmolo Pasquale²^ORCID,Tagliaferri Roberto¹²,Ferraro Pietro²^ORCID

Affiliation:

1. Neurone Lab, Department of Management and Innovation Systems (DISA-MIS), University of Salerno, Fisciano, Italy

2. CNR – Institute of Applied Sciences and Intelligent Systems “Eduardo Caianiello”, Pozzuoli, Italy

3. Key Laboratory of Precision Opto-Mechatronics Technology of Ministry of Education, School of Instrumentation Science & Optoelectronics Engineering, Beihang University, 100191 Beijing, China

4. Department of Obstetrics and Gynecology, Peking University People's Hospital, Beijing 100044, China

Abstract

Unbiased learning pipeline for label-free single-cell classification.

Funder

Ministero dell’Istruzione, dell’Università e della Ricerca

Publisher

Royal Society of Chemistry (RSC)

Subject

Biomedical Engineering,General Chemistry,Biochemistry,Bioengineering

Link

http://pubs.rsc.org/en/content/articlepdf/2024/LC/D3LC00385J

Reference66 articles.

1. Label-free cell cycle analysis for high-throughput imaging flow cytometry

2. Diagnostic Potential of Imaging Flow Cytometry

3. Perspectives on liquid biopsy for label‐free detection of “circulating tumor cells” through intelligent lab‐on‐chips

4. Characterization of Protein Particles in Therapeutic Formulations Using Imaging Flow Cytometry

5. A novel method for measuring cellular antibody uptake using imaging flow cytometry reveals distinct uptake rates for two different monoclonal antibodies targeting L1