Sizing Cells Using Acoustic Flow Cytometry
Bibliographic record
Abstract
We have developed an acoustic flow cytometer that combines ultrasound and microfluidics to rapidly size and count cells label-free in a high-throughput manner. Cells are hydrodynamically focused single file into a narrow stream through an ultrasound beam where ultrasound is scattered from each individual cell. The ultrasound operates at a center frequency of 375 MHz with a wavelength of 4 μm; in this regime, the power spectra of the backscattered ultrasound waves have distinct features at specific frequencies that are directly related to the cell size. Our approach can be used to determine the cell diameter by comparing these unique spectral features to established analytical solutions of ultrasound scattering. 3 μm beads with known scattering properties were used to validate the technique. Then, we examined two cells lines with different average cell diameters; acute myeloid leukemia cells, where 2,390 measurements resulted in a mean diameter of 10.0 ± 1.7 μm, and HT29 colorectal cancer cells, where 1,955 measurements resulted in a mean diameter of 15.0 ± 2.3 μm, The resulting histograms showed anticipated distributions. Microfluidic based devices are commonly used for probing single cells, however, very few can determine the cell size. Our technique combines ultrasound and microfluidics to accurately determine the cell size on a cell-by-cell basis, with the potential for integration with fluorescence, light scattering and quantitative photoacoustic techniques for a multiparameter cellular characterization method.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".