Characterization of red blood cell aggregation with photoacoustics: A theoretical and experimental study
Bibliographic record
Abstract
The aggregation of red blood cells (RBCs) is a phenomenon that is governed by plasma fibrinogen concentration and the shear forces of flow. We propose the use of photoacoustics (PA) for the detection and characterization of RBC aggregation. A 2D simulation study was performed to investigate the dependence of the PA signal on hematocrit for non-aggregated cells and on the aggregate size for aggregated samples. Experimental confirmation of theoretical results was conducted using human RBC samples and the Imagio PA imaging device (Seno Medical Instruments Inc., San Antonio, TX). Samples were exposed to 1064 nm laser irradiation. PA signals were collected from the non-aggregated samples at varying hematocrit levels. Aggregation was induced by suspending the RBCs in various concentrations of Dextran-70. To account for the response of the imaging system, the PA spectra were calibrated by dividing by the response of the transducer measured using a needle hydrophone. Theoretical and experimental results show a monotonic increase in the PA signal amplitude with increasing hematocrit. As the size of aggregates increases, simulations demonstrate a shift towards lower frequencies in the PA power spectrum as well as enhancements as large as 11 dB compared to the non-aggregated sample. Experimental results confirm the ability of PA to detect and quantify the aggregation of RBCs. PA radiofrequency spectral analysis seems provides a quantitative means for distinguishing between aggregated and non-aggregated samples.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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".