High-Intensity Focused Ultrasound (HIFU) for Dissolution of Clots in a Rabbit Model of Embolic Stroke
Bibliographic record
Abstract
It is estimated that only 2-6% of patients receive thrombolytic therapy for acute ischemic stroke suggesting that alternative therapies are necessary. In this study, we investigate the potential for high intensity focused ultrasound (HIFU) to initiate thrombolysis in an embolic model of stroke. Iron-loaded blood clots were injected into the middle cerebral artery (MCA) of New Zealand White rabbits, through the internal carotid artery and blockages were confirmed by angiography. MRI was used to localize the iron-loaded clot and target the HIFU beam for treatment. HIFU pulses (1.5 MHz, 1 ms bursts, 1 Hz pulse repetition frequency, 20 s duration) were applied to initiate thrombolysis. Repeat angiograms and histology were used to assess reperfusion and vessel damage. Using 275 W of acoustic power, there was no evidence of reperfusion in post-treatment angiograms of 3 rabbits tested. In a separate group of animals, 415 W of acoustic power was applied and reperfusion was observed in 2 of the 4 (50%) animals treated. In the last group of animals, acoustic power was further increased to 550 W, which led to the reperfusion in 5 of 7 (∼70%) animals tested. Histological analysis confirmed that the sonicated vessels remained intact after HIFU treatment. Hemorrhage was detected outside of the sonication site, likely due to the proximity of the target vessel with the base of the rabbit skull. These results demonstrate the feasibility of using HIFU, as a stand-alone method, to cause effective thrombolysis without immediate damage to the targeted vessels. HIFU, combined with imaging modalities used to identify and assess stroke patients, could dramatically reduce the time to achieve flow restoration in patients thereby significantly increasing the number of patients which benefit from thrombolysis treatments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".