Photoacoustic ultrasound spectroscopy for assessing red blood cell aggregation and oxygenation
Bibliographic record
Abstract
Red blood cell (RBC) aggregation and oxygenation are important markers for a variety of blood disorders. No current technique is capable of simultaneously measuring aggregation/oxygenation levels noninvasively. We propose using photoacoustic ultrasound spectroscopy (PAUS) for assessing both phenomena. This technique relies on frequency-domain analysis of the PA signals by extracting parameters such as the ultrasound spectral slope and the midband fit. To investigate the effect of hematocrit, aggregation, and oxygenation levels on PAUS parameters, a Monte Carlo-based theoretical model and an experimental protocol using porcine RBCs were developed. The samples were illuminated at 750 and 1064 nm and changes in the PAUS parameters were compared to the oxygen-dependent optical absorption coefficients to assess the oxygenation level. Good agreement between the theoretical and experimental spectral parameters was obtained for the spectral slope of the nonaggregated spectra (≈ 0.3 dB/MHz). The experimental midband fit increased by ≈ 5 dB for the largest aggregate size. Based on the analysis of the PA signals, the oxygen saturation level of the most aggregated sample was >20% greater than the nonaggregated sample. The results provide a framework for using PA signals' spectroscopic parameters for monitoring the aggregation and oxygenation levels of RBCs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".