Low-Intensity MR-Guided Focused Ultrasound Mediated Disruption of the Blood-Brain Barrier for Intracranial Metastatic Diseases
Bibliographic record
Abstract
Low-intensity MR-guided focused ultrasound in combination with intravenously injected microbubbles is a promising platform for drug delivery to the central nervous system past the blood-brain barrier. The blood-brain barrier is a key bottleneck for cancer therapeutics via limited inter- and intracellular transport. Further, drugs that cross the blood-brain barrier when delivered in a spatially nonspecific way, result in adverse effects on normal brain tissue, or at high concentrations, result in increasing risks to peripheral organs. As such, various anti-cancer drugs that have been developed or to be developed in the future would benefit from a noninvasive, temporary, and repeatable method of targeted opening of the blood-brain barrier to treat metastatic brain diseases. MR-guided focused ultrasound is a potential solution to these design requirements. The safety, feasibility and preliminary efficacy of MRgFUS aided delivery have been demonstrated in various animal models. In this review, we discuss this preclinical evidence, mechanisms of focused ultrasound mediated blood-brain barrier opening, and translational efforts to neuro-oncology patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".