Ultrasound elastography monitoring reveals a decline in shear wave attenuation and clot viscosity after recombinant tissue plasminogen activator treatment of blood clots
Bibliographic record
Abstract
Deep vein thrombosis is one of the leading causes of disability and serious illness and can become fatal. The lytic recombinant tissue plasminogen activator (rt-PA) is the main drug used for clot lysis and rapid normalization of venous blood flow. Less than 40% of patients who receive rt-PA treatment have improved blood flow. In this study, we quantitatively monitor the rt-PA treatment in in-vitro blood clots with ultrasound elastography. An acoustic radiation force imaging sequence was implemented on a research ultrasound system to remotely generate shear waves inside the blood clot samples. Porcine blood samples from two different pigs were bought from a local slaughterhouse. Clots of varying viscoelasticity were prepared by allowing different coagulation time between 30 min to 3 days in borosilicate glass pipettes. The clots were then embedded in gelatin-agar phantoms to perform ultrasound measurements. Another batch of clots from the same blood samples was treated with rt-PA drug for 30 min, and ultrasound measurements were performed after treatment. In 11 samples, variations in SW speed before and after rt-PA treatment were not found to be statistically significant (>0.05). However, SW attenuation and clot viscosity declined significantly after rt-PA treatment by 33.49% ± 31.07% (<0.05) and 33.33% ± 35.72% (<0.05). The results indicate that SW attenuation and viscosity can be used to monitor DVT treatment, and clot viscosity may emerge as an important biomarker for clot staging.
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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 | 0.000 | 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".