Experimental ultrasound characterization of red blood cell aggregation using the structure factor size estimator
Bibliographic record
Abstract
The frequency dependence of the ultrasonic backscattering coefficient (BSC) was studied to assess the level of red blood cell (RBC) aggregation. Three monoelement focused wideband transducers were used to insonify porcine blood sheared in a Couette flow from 9 to 30 MHz. A high shear rate was first applied to promote disaggregation. Different residual shear rates were then used to promote formation of RBC aggregates. The structure factor size estimator (SFSE), a second-order data reduction model based on the structure factor, was applied to the frequency-dependent BSC. Two parameters were extracted from the model to describe the level of aggregation at 6% and 40% hematocrits: W, the packing factor, and D the aggregate diameter, expressed in number of RBCs. Both parameters closely matched theoretical values for nonaggregated RBCs. W and D increased during aggregation with stabilized values modulated by the applied residual shear rate. Furthermore, parameter D during the kinetics of aggregation at 6% hematocrit under static conditions correlated with an optical RBC aggregate size estimation from microscopic images (r(2)=0.76). To conclude, the SFSE presents an interesting framework for tissue characterization of partially correlated dense tissues such as aggregated 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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 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".