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TRUVACE RECORD VERSION
record: TRV-2026-1170
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-22T06:54:04.055883Z
status: published
lens: g_space
sector: science
headline: Code-multiplexed multi-frequency impedance cytometry with a unified deep-unfolding network
dek: Impedance flow cytometry (IFC) is a label-free, single-cell measurement technique that captures biophysical properties beyond traditional biochemical markers. Code-multiplexing allows parallelization of IFC with simple hardware but requires advanced signal processing algorithms to resolve overlaps in signals originating from different channels. Existing methods, however, rely on multiple task-specific networks with template-based linear fitting, which loses accuracy under nonlinear or unstable conditions common…
gain_title: We unfold the successive-interference cancellation (SIC) algorithm into a deep-learning network, where repeated stages of a single multitask network implement iterative signal estimation and interference cancellation that reflect the structural prior of SIC.
problem_title: (none)
trace_subject: (none)
gain_reading: We unfold the successive-interference cancellation (SIC) algorithm into a deep-learning network, where repeated stages of a single multitask network implement iterative signal estimation and interference cancellation that reflect the structural prior of SIC.
gain_evidence: (none)
problem_reading: (none)
problem_evidence: (none)
quick_read: Impedance flow cytometry (IFC) is a label-free, single-cell measurement technique that captures biophysical properties beyond traditional biochemical markers. Code-multiplexing allows parallelization of IFC with simple hardware but requires advanced signal processing algorithms to resolve overlaps in signals originating from different channels.

We unfold the successive-interference cancellation (SIC) algorithm into a deep-learning network, where repeated stages of a single multitask network implement iterative signal estimation and interference cancellation that reflect the structural prior of SIC.
limitation: 
tag: Evidence-backed gain
key_points: Impedance flow cytometry (IFC) is a label-free, single-cell measurement technique that captures biophysical properties beyond traditional biochemical markers. | Code-multiplexing allows parallelization of IFC with simple hardware but requires advanced signal processing algorithms to resolve overlaps in signals originating from different channels. | Existing methods, however, rely on multiple task-specific networks with template-based linear fitting, which loses accuracy under nonlinear or unstable conditions common in microfluidic experiments.
rundown: Impedance flow cytometry (IFC) is a label-free, single-cell measurement technique that captures biophysical properties beyond traditional biochemical markers. Code-multiplexing allows parallelization of IFC with simple hardware but requires advanced signal processing algorithms to resolve overlaps in signals originating from different channels.

Existing methods, however, rely on multiple task-specific networks with template-based linear fitting, which loses accuracy under nonlinear or unstable conditions common in microfluidic experiments. Prior studies have also been restricted to demultiplexing single-frequency impedance measurements.
sources:
- peer_reviewed | Microsystems & Nanoengineering | https://doi.org/10.1038/s41378-026-01420-z | 2026-09-21
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