Advances in acoustic monitoring and control of focused ultrasound-mediated increases in blood-brain barrier permeability
Bibliographic record
Abstract
Transcranial focused ultrasound (FUS) combined with intravenously circulating microbubbles can transiently and selectively increase blood-brain barrier permeability to enable targeted drug delivery to the central nervous system, and is a technique that has the potential to revolutionize the way neurological diseases are managed in medical practice. Clinical testing of this approach is currently underway in patients with brain tumors, early Alzheimer's disease, and amyotrophic lateral sclerosis. A major challenge that needs to be addressed in order for widespread clinical adoption of FUS-mediated blood-brain barrier permeabilization to occur is the development of systems and methods for real-time treatment monitoring and control, to ensure that safe and effective acoustic exposure levels are maintained throughout the procedures. This review gives a basic overview of the oscillation dynamics, acoustic emissions, and biological effects associated with ultrasound-stimulated microbubbles in vivo, and provides a summary of recent advances in acoustic-based strategies for detecting, controlling, and mapping microbubble activity in the brain. Further development of next-generation clinical FUS brain devices tailored towards microbubble-mediated applications is warranted and required for translation of this potentially disruptive technology into routine clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".