A new method to assess the kinetics of rouleaux formation in human subcutaneous veins using high frequency parametric imaging: preliminary results
Bibliographic record
Abstract
Laser erythroaggregameter is considered as the gold standard method for in vitro red blood cell (RBC) aggregation characterization. With this method, a high shear rate is first applied on the blood sample to disrupt the aggregates and to provide a common reference level prior to the aggregation phases. In vivo, it is very difficult to have a standard protocol that would allow studying the kinetics of rouleaux formation following prior disruption of the aggregates. We propose a new approach using low frequency shear waves to initially disrupt the aggregates, followed by the measurement of the kinetics of aggregation with high-frequency ultrasound in a peripheral subcutaneous vein following flow stoppage. Radio frequency (RF) data was recorded and absolute backscatter coefficient (BSC) and spectral slope (SS) were calculated on video sequences of 25 s for two normal individuals. BSC and SS varied over time (p < 0.001) and differed between subjects (p < 0.001), due to the different aggregability of RBCs between the two individuals. This later result was corroborated by the laser aggregameter method. Immediately after the transmission of shear waves, no significant differences between groups were found for BSC and SS, suggesting that this approach effectively provides a common reference level of disaggregation. In conclusion, it is shown that shear wave propagation in the subcutaneous vein allows disaggregation of RBCs and the obtaining of a common reference level to allow reproducible measurements of BSC and SS during the kinetic of rouleaux formation. It is believed that this method could be of clinical significance for in vivo measurements in pathologies associated with hyper-aggregation of erythrocytes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".