Enhancement of brain hyperthermia via transcranial magnetic resonance imaging-guided focused ultrasound and microbubbles—Heating mechanism investigation using COMSOL
Bibliographic record
Abstract
Noninvasive methods for enhancing the brain drug delivery has been pursued for years. Previously we developed a new MR-guided focused ultrasound (FUS)-based technique, which can achieve targeted brain hyperthermia for heat-triggered drug release and simultaneously open the blood–brain barrier safely for drug penetration. However, the underline mechanisms were unclear. This study aimed to explore the mechanisms for the enhanced FUS brain tissue hyperthermia with microbubbles via numerical modeling in COMSOL. The acoustic wave equation was employed to describe the FUS propagation. A bubble dynamics equation was adopted for calculating the stable bubble oscillations under FUS exposures. A modified bioheat transfer equation was utilized to compute the heating, with various heating sources including FUS, microbubble acoustic emission (MAE), and viscous dissipation (VD). The microbubbles were randomly distributed within the focal region. The sonication time was 6s with an initial temperature of 41°C. The average temperature in the focal region were 41.65°C, 42.24°C, 42.98°C, and 43.59°C for FUS alone, FUS + MAE, FUS + VD, and FUS + MAE + VD, respectively. Compared with the FUS alone, both MAE and VD made significant contributions to the heating with additional temperature increases of 47.6% and 67.2%, respectively.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".