Classification of biological cells using a sound wave based flow cytometer
Bibliographic record
Abstract
A flow cytometer that uses sound waves to determine the size of biological cells is presented. In this system, a microfluidic device made of polydimethylsiloxane (PDMS) was developed to hydrodynamically flow focus cells in a single file through a target area. Integrated into the microfluidic device was an ultrasound transducer with a 375 MHz center frequency, aligned opposite the transducer was a pulsed 532 nm laser focused into the device by a 10x objective. Each passing cell was insonfied with a high frequency ultrasound pulse, and irradiated with the laser. The resulting ultrasound and photoacoustic waves from each cell were analyzed using signal processing methods, where features in the power spectra were compared to theoretical models to calculate the cell size. Two cell lines with different size distributions were used to test the system: acute myeloid leukemia cells (AML) and melanoma cells. Over 200 cells were measured using this system. The average calculated diameter of the AML cells was 10.4 ± 2.5 μm using ultrasound, and 11.4 ± 2.3 μm using photoacoustics. The average diameter of the melanoma cells was 16.2 ± 2.9 μm using ultrasound, and 18.9 ± 3.5 μm using photoacoustics. The cell sizes calculated using ultrasound and photoacoustic methods agreed with measurements using a Coulter Counter, where the AML cells were 9.8 ± 1.8 μm and the melanoma cells were 16.0 ± 2.5 μm. These results demonstrate a high speed method of assessing cell size using sound waves, which is an alternative method to traditional flow cytometry techniques.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".