Optimizing parameters for ultrasound-induced disruption of the blood-brain barrier.
Bibliographic record
Abstract
Microbubble-mediated ultrasound-induced blood-brain barrier disruption (BBBD) is a promising and increasingly investigated technique for the targeted delivery of therapeutics in the brain. Currently in the pre-clinical stages of research, there is a need to establish treatment parameters which will produce consistent and safe BBBD. The BBB was disrupted in rats at 1.18 MHz using modified pulses which have been shown to eliminate acoustic standing waves in the skull cavity. 10-ms bursts consisting of single excitation cycles separated by a set interval were repeated at 1 Hz for 2 min. The transducer used (10-cm aperture, FN=0.8) rang for approximately 3 μs following a single cycle at 1.18 MHz. The interval between cycles was varied using values of 6, 60, 300, 600 μs, and a single cycle every second. Enhancement levels measured via contrast-enhanced T1W MRI decreased as the time interval was increased. This did not appear proportional to the time-averaged acoustic power, and a single excitation cycle every second successfully caused BBBD. Enhancement levels following 5-min sonications with bolus injection of microbubbles versus those with slow infusion were also compared. Enhancement levels were higher following bolus; however, sonications with infusion produced more consistent enhancement levels.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".