A Monte Carlo study on the effects of erythrocyte oxygenation on photoacoustic signals
Bibliographic record
Abstract
A theoretical model to study the effects of erythrocyte oxygenation on photoacoustic (PA) signals is described. An erythrocyte was considered as a fluid sphere and for such a sphere the PA field was computed by using a frequency domain approach. The linear superposition principle was used to obtain the resultant PA field generated by a collection of red blood cells (RBCs). A Monte Carlo algorithm was used to simulate 2D tissue realizations consisting of oxygenated RBCs (RBCOs) and deoxygenated RBCs (RBCDs). The oxygen saturation level of RBCOs was assumed to be 100% and 0% for RBCDs. The proportion of RBCOs and RBCDs fixed the oxygen saturation (SO2) of a blood sample as, SO2= NO/(NO+ ND), where NOand NDrepresent the numbers of RBCOs and RBCDs. The simulation results showed that the mean PA signal amplitude decreased monotonically as the SO2level increased for the 700 nm laser radiation. The same quantity exhibited a monotonic rise as the SO2level increased for the 1000 nm optical source. The PA amplitude demonstrated nearly 6 fold decrease and 5 fold increase, respectively at those wavelengths when SO2level varied from 0 to 100%. Spectral intensity in the low frequency range (2. However, these trends were not distinctly observed between 10-100 MHz. The simulated trends were in accordance with other experimental works. This suggests the suitability of this formulation to model the PA signal behaviors at different SO2levels.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".