− LIFESTYLE Precision nutrition on a global scale necessitates an understanding of food not as static collections of so-c…+ SCIENCE Impedance flow cytometry (IFC) is a label-free, single-cell measurement technique that captures biophysical p…+ HEALTH Machine learning is increasingly reshaping psychological and mental health research by complementing traditio…− HEALTH Generative artificial intelligence in hematology is entering a new phase. The dominant question, whether larg…− CRIME Background Percutaneous coronary intervention effectively treats flow-limiting stenoses; however, untreated n…− HEALTH Objective To evaluate the real-world diagnostic performance of a multimodal large language model (LLM) for im…− HEALTH Artificial intelligence (AI) is increasingly present in dental education and clinical practice through system…+ HEALTH The diagnosis of major depressive disorder (MDD) currently relies on subjective clinical assessment, undersco…
TruaceTracing the truth around AITuesday, September 22, 2026
Subscribe
TRV-2026-1170Version 1 · Certified

Written 2026-09-22 06:54:04 UTC · current record

Reason for this version

Certified into the record

Canonical text (the exact bytes fingerprinted)

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
prev: 0000000000000000000000000000000000000000000000000000000000000000
sha256
52205b467d864893a6566edbace8497d3916796a837f89a21332f21b660f4ea9
previous
0000000000000000000000000000000000000000000000000000000000000000
Verify this record
How to verify without trusting this page

Fetch the canonical text of any version from /api/record/TRV-2026-1170 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.