Closed-loop cavitation control for focused ultrasound-mediated blood–brain barrier opening by long-circulating microbubbles
Bibliographic record
Abstract
Focused ultrasound (FUS) exposure in the presence of microbubbles (MBs) has been successfully used in the delivery of various sizes of therapeutic molecules across the blood-brain barrier (BBB). While acoustic pressure is correlated with the BBB opening size, real-time control of BBB opening to avoid vascular and neural damage is still a challenge. This arises mainly from the variability of FUS-MB interactions due to the variations of animal-specific metabolic environment and specific experimental setup. In this study, we demonstrate a closed-loop cavitation control framework to induce BBB opening for delivering large therapeutic molecules without causing macro tissue damages. To this end, we performed in mice long-term (5 min) cavitation monitoring facilitated by using long-circulating MBs. Monitoring the long-term temporal kinetics of the MBs under varying level of FUS pressure allowed to identify in situ, animal specific activity regimes forming pressure-dependent activity bands. This enables to determine the boundaries of each activity band (i.e. steady oscillation, transition, inertial cavitation) independent from the physical and physiological dynamics of the experiment. However, such a calibration approach is time consuming and to speed up characterization of the in situ, animal specific FUS-MB dynamics, we tested a novel method called 'pre-calibration' that closely reproduces the results of long-term monitoring but with a much shorter duration. Once the activity bands are determined from the pre-calibration method, an operation band can be selected around the desired cavitation dose. To drive cavitation in the selected operation band, we developed an adaptive, closed-loop controller that updates the acoustic pressure between each sonication based on measured cavitation dose. Finally, we quantitatively assessed the safety of different activity bands and validated the proposed methods and controller framework. The proposed framework serves to optimize the FUS pressure instantly to maintain the targeted cavitation level while improving safety control.
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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 | 0.000 |
| 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) | 0.000 | 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".