Microbubble formulation influences inflammatory response to focused ultrasound exposure in the brain
Bibliographic record
Abstract
Focused ultrasound and microbubble (FUS + MB)-mediated blood-brain barrier (BBB) permeability enhancement can facilitate targeted brain-drug delivery. While controlling the magnitude of BBB permeability enhancement is necessary to limit tissue damage, little work has attempted to decouple these concepts. This work investigated the relationship between BBB permeability enhancement and the relative transcription of inflammatory mediators 4 h following sonication. Three microbubble formulations, Definity, BG8774, and MSB4, were compared, with the dose of each formulation normalized to gas volume. While changes in the transcription of key proinflammatory mediators, such as Il1b, Ccl2, and Tnf, were correlated to the magnitude of BBB permeability enhancement, these correlations were not independent of microbubble formulation; microbubble size distribution may play an important role, as linear regression analyses of BBB permeability magnitude versus differential gene expression for these proinflammatory mediators revealed significantly greater slopes for MSB4, a monodisperse microbubble with mean diameter of 4 μm, compared to Definity or BG8774, both polydisperse microbubbles with mean diameters below 2 μm. Additionally, the function of an acoustic feedback control algorithm, based on the detection threshold of ultraharmonic emissions, was assessed. While this control strategy was effective in limiting both wideband emissions and red blood cell extravasation, microbubble formulation was found to influence the magnitude of BBB leakage and correlations to acoustic emissions. This work demonstrates that while the initial magnitude of FUS + MB-mediated BBB permeability enhancement has a clear influence on the subsequent inflammatory responses, microbubble characteristics influence these relationships and must also be considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".