Drug delivery across the blood–brain barrier using focused ultrasound
Bibliographic record
Abstract
INTRODUCTION: The presence of the blood-brain barrier (BBB) is a significant impediment to the delivery of therapeutic agents to the brain for treatment of brain diseases. Focused ultrasound (FUS) has been developed as a noninvasive method for transiently increasing the permeability of the BBB to promote drug delivery to targeted regions of the brain. AREAS COVERED: The present review briefly compares the methods used to promote drug delivery to the brain and describes the benefits and limitations of FUS technology. We summarize the experimental data which shows that FUS, combined with intravascular microbubbles, increases therapeutic agent delivery into the brain leading to significant reductions in pathology in preclinical models of disease. The potential for translation of this technology to the clinic is also discussed. EXPERT OPINION: The introduction of magnetic resonance imaging guidance and intravascular administration of microbubbles to FUS treatments permits the consistent, transient and targeted opening of the BBB. The development of feedback systems and real-time monitoring techniques improve the safety of BBB opening. Successful clinical translation of FUS has the potential to revolutionize the treatment of brain disease resulting in effective, less-invasive treatments without the need for expensive drug development.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".