Simultaneous ultrasound and photoacoustics based flow cytometry
Bibliographic record
Abstract
We have developed a flow cytometer based on simultaneous detection of ultrasound and photoacoustic waves from individual particles/cells flowing in a microfluidic channel. Our polydimethylsiloxane (PDMS) based hydrodynamic 3-dimensional (3D) flow-focusing microfluidic device contains a cross-junction channel, a micro-needle (ID 100 μm and OD 200 μm) insert, and a 3D printed frame to hold and align a high frequency (center frequency 375 MHz) ultrasound transducer. The focused flow passes through a narrow focal zone with lateral and axial focal lengths of 6-8 μm and 15-20 μm, respectively. Both the lateral and axial alignments are achieved by screwing the transducer to the frame onto the PDMS device. Individual particles pass through an interrogation zone in the microfluidic channel with a collinearly aligned ultrasound transducer and a focused 532 nm wavelength laser beam. The particles are simultaneously insonified by high-frequency ultrasound and irradiated by a laser beam. The ultrasound backscatter and laser generated photoacoustic waves are detected for each passing particle. The backscattered ultrasound and photoacoustic signal are strongly dependent on the size, morphology, mechanical properties, and material properties of the flowing particles; these parameters can be extracted by analyzing unique features in the power spectrum of the signals. Frequencies less than 100 MHz do not have these unique spectral signatures. We show that we can reliably distinguish between different particles in a sample using the acoustic-based flow cytometer. This technique, when extended to biomedical applications, allows us to rapidly analyze the spectral signatures from individual single cells of a large cell population, with applications towards label-free detection and characterization of healthy and diseased cells.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".