Neural Network‐Enabled Multiparametric Impedance Signal Templating for High throughput Single‐Cell Deformability Cytometry Under Viscoelastic Extensional Flows (Small 5/2025)
Neural Networks Neural networks that couple the accuracy of cell morphology imaging to the speed of impedance signal templating are presented for extraction of cellular biophysical metrics during flow cytometry to distinguish cellular subpopulations on deformability, viability and electrical physiology properties More in article number 2407212, Federica Caselli, Nathan S. Swami, and co-workers.
Authors
- Shaghayegh Mirhosseini (ORCID: https://orcid.org/0000-0002-7385-8142)
- Sara J. Adair (ORCID: https://orcid.org/0000-0002-9973-4252)
- Federica Caselli (ORCID: https://orcid.org/0000-0001-6663-8603)
- Abdullah‐Bin Siddique (ORCID: https://orcid.org/0009-0004-8159-3633)
- Aditya Rane (ORCID: https://orcid.org/0000-0002-1882-3609)
- Todd W. Bauer (ORCID: https://orcid.org/0000-0002-9516-1848)
- Nathan S. Swami (ORCID: https://orcid.org/0000-0002-0492-1160)
- Javad Jarmoshti
Institutions
- University of Rome Tor Vergata (IT)
- University of Virginia (US)
Publication Details
- Journal
- Small
- Published
- 2025-02-01
- DOI
- https://doi.org/10.1002/smll.202570038
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00