Abstract 14673: The Effect of Increasing the Ultrasound Pulse Length on Sonoreperfusion Therapy
Bibliographic record
Abstract
Background: In recent years, long and short pulse ultrasound (US)-targeted microbubble cavitation (UTMC) has been shown to increase perfusion in healthy and ischemic skeletal muscle, in pre-clinical animal models of microvascular obstruction, and in the myocardium of patients presenting with acute myocardial infarction, and microvascular vasodilation played an important role in its efficacy. Our prior preliminary data suggested that long pulse US confers enhanced sonoreperfusion efficacy compared to short pulse US. Objective: In this study, we sought to definitively compare acoustic energy matched, long (5000 cycles) and short pulse (500 х 10 cycles, separated by 100 μs intervals) US (pressure of 1.5 MPa with an equivalent total number of acoustical cycles and at the same frequency (1 MHz)), in a rodent hindlimb model, with and without microvascular obstruction (MVO). Results: By quantifying perfusion using burst replenishment contrast enhanced US imaging, we found that: (1) long and short pulses result in different vasodilation kinetics in an intact hindlimb model. The long pulse causes an initial reduction in flow due to microvascular spasm that spontaneously resolved at 4-min, followed by sustained higher flow rates (~2-folds) compared to baseline, starting 10 min after therapy ( p <0.05). The short pulse caused a short-lived ~2-fold increase in flow rate that peaked at 4-min ( p <0.05), but without the initial microvascular spasm; (2) the sustained increase in perfusion during long pulse is not simply reactive hyperemia; and (3) both pulses are effective in reperfusion of MVO in our hindlimb model by restoring blood volume, but only the long pulse caused an increase in flow rate after treatment 2, compared to MVO ( p <0.05). Histological analysis post UTMC with either pulse configuration indicates no evidence of tissue damage or hemorrhage. Conclusions: Our findings demonstrate that the microbubble oscillation induces vasodilation and therapeutic efficacy for the treatment of MVO can be tuned by varying pulse length; relative to short pulse US, longer pulses drive greater microbubble cavitation and more rapid microvascular flow rate restoration after MVO, warranting further optimization of the pulse length for sonoreperfusion therapy.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".