Evaluating the safety profile of focused ultrasound and microbubble-mediated treatments to increase blood-brain barrier permeability
Bibliographic record
Abstract
Introduction: Treatment of several diseases of the brain are complicated by the presence of the skull and the blood-brain barrier (BBB). Focused ultrasound (FUS) and microbubble (MB)-mediated BBB treatment is a minimally invasive method to transiently increase the permeability of blood vessels in targeted brain areas. It can be used as a general delivery system to increase the concentration of therapeutic agents in the brain parenchyma.Areas covered: Over the past two decades, the safety of using FUS+MBs to deliver agents across the BBB has been interrogated through various methods of imaging, histology, biochemical assays, and behavior analyses. Here we provide an overview of the factors that affect the safety profile of these treatments, describe methods by which FUS+MB treatments are controlled, and discuss data that have informed the assessment of treatment risks.Expert opinion: There remains a need to assess the risks associated with clinically relevant treatment strategies, specifically repeated FUS+MB treatments, with and without therapeutic agent delivery. Additionally, efforts to develop metrics by which FUS+MB treatments can be easily compared across studies would facilitate a more rapid consensus on the risks associated with this intervention.Trial registration: ClinicalTrials.gov identifier: NCT03321487.Trial registration: ClinicalTrials.gov identifier: NCT03119961.Trial registration: ClinicalTrials.gov identifier: NCT02343991.Trial registration: ClinicalTrials.gov identifier: NCT03608553.Trial registration: ClinicalTrials.gov identifier: NCT03626896.Trial registration: ClinicalTrials.gov identifier: NCT03616860.Trial registration: ClinicalTrials.gov identifier: NCT03671889.Trial registration: ClinicalTrials.gov identifier: NCT03712293.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".