Resonance, Velocity, Dispersion, and Attenuation of Ultrasound‐Induced Shear Wave Propagation in Blood Clot In Vitro Models
Bibliographic record
Abstract
Objective Improve the characterization of mechanical properties of blood clots. Parameters derived from shear wave (SW) velocity and SW amplitude spectra were determined for gel phantoms and in vitro blood clots. Methods Homogeneous phantoms and phantoms with gel or blood clot inclusions of different diameters and mechanical properties were analyzed. SW amplitude spectra were used to observe resonant peaks. Parameters derived from those resonant peaks were related to mimicked blood clot properties. Three regions of interest were tested to analyze where resonances occurred the most. For blood experiments, 20 samples from different pigs were analyzed over time during a 110‐minute coagulation period using the Young modulus, SW frequency dispersion, and SW attenuation. Results The mechanical resonance was manifested by an increase in the number of SW spectral peaks as the inclusion diameter was reduced ( P < .001). In blood clot inclusions, the Young modulus increased over time during coagulation ( P < .001). Descriptive spectral parameters (frequency peak, bandwidth, and distance between resonant peaks) were linearly correlated with clot elasticity values ( P < .001) with R 2 = .77 for the frequency peak, .60 for the bandwidth, and .48 for the distance between peaks. The SW dispersion and SW attenuation reflecting the viscous behavior of blood clots decreased over time ( P < .001), mainly in the early stage of coagulation (first minutes). Conclusion The confined soft inclusion configuration favored SW mechanical resonances potentially challenging the computation of spectral‐based parameters, such as the SW attenuation. The impact of resonances can be reduced by properly selecting the region of interest for data analysis.
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 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 | 0.000 | 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".