Cavitation Feedback Control of Focused Ultrasound Blood-Brain Barrier Opening for Drug Delivery in Patients with Parkinson’s Disease
Bibliographic record
Abstract
Magnetic resonance-guided focused ultrasound (MRgFUS), in conjunction with circulating microbubbles, is an emerging technology that can transiently enhance the permeability of the blood-brain barrier (BBB) locally and non-invasively to facilitate targeted drug delivery to the brain. In this clinical trial, the feasibility and safety of BBB modulation in the putamen were evaluated for biweekly therapeutic agent delivery in patients with Parkinson's disease. The performance of the clinical MRgFUS system's cavitation feedback controller for active power modulation throughout the exposures was examined. The putamen was targeted unilaterally by an ExAblate Neuro MRgFUS system operating at 220 kHz. Definity microbubbles were infused via a saline bag gravity drip at a rate of 4 µL/kg per 5 min. A cavitation emissions-based feedback controller was employed to modulate the acoustic power automatically according to prescribed target cavitation dose levels. BBB opening was measured by Gadolinium (Gd)-enhanced T1-weighted MR imaging, and the presence of potential micro-hemorrhages induced by the exposures was assessed via T2*-weighted MR imaging. A total of 12 treatment sessions were carried out across four patients, with target cavitation dose levels ranging from 0.20-0.40. BBB permeability in the targeted putamen was elevated successfully in all treatments, with a 14% ± 6% mean increase in Gd-enhanced T1-weighted MRI signal intensity relative to the untreated contralateral side. No indications of red blood cell extravasations were observed on MR imaging scans acquired one day following each treatment session. The cavitation emissions-based feedback controller was effective in modulating acoustic power levels to ensure BBB permeability enhancement while avoiding micro-hemorrhages, however, further technical advancements are warranted to improve its performance for use across a wide variety of brain diseases.
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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) | 0.001 | 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".