Dielectric Analysis of Single Biological Cells at Both Microwave and Optical Frequencies
Bibliographic record
Abstract
Dielectric measurement of a biological cell at microwave frequency or optical frequency provides unique yet distinct information about its physiological state. Microwave dielectric spectroscopy gives information on plasma membrane complexity and permeability, cytoplasm and nucleoplasm ion concentration, and the presence of smaller membrane-bound organelles. Optical holographic imaging and reconstruction provides complementary information on cell morphology and refractive index, which reflects cytoplasm and nucleoplasm mass density. As shown in Fig. 1a, we use a microfluidic device for dual-modality RF dielectrophoresis (DEP) and optical scattering measurements of single biological cells while in flow. (E. Kovacs, et al., “Light-Emitting Diode Array with Optical Linear Detector Enables High-Throughput Differential Single-Cell Dielectrophoretic Analysis,” Sensors, vol. 24, issue 24, 8071, 2024). Cells flow through a channel and over electrodes, which induce an RF frequency-dependent DEP force that translates them vertically to higher or lower velocity regions in the channel. Multiple LED semi-coherent optical sources above the channel illuminate the cells. A linear CMOS imaging array below the channel captures the resulting inline incident-scattered field interference patterns. The captured holograms are analyzed to reconstruct each cell's size and optical refractive index. Simultaneously, the cell's DEP induced velocity trajectory, obtained by particle tracking, is used to analyze selected RF dielectric properties. (S. Afshar, et al., “Full Beta-Dispersion Region Dielectric Spectra and Dielectric Models of Viable and Non-Viable CHO Cells,” IEEE J. Electromagnetics, RF, Microwaves in Medicine and Biology, vol. 5, issue 1, 2021).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".