Microbubble-assisted MRI-guided focused ultrasound for hyperthermia at reduced power levels
Bibliographic record
Abstract
PURPOSE: Ultrasound contrast agent microbubbles were combined with magnetic resonance imaging (MRI)-guided focused ultrasound (MRgFUS) as a means to achieve mild hyperthermia at reduced power levels. METHODS: MRgFUS hyperthermia (42°C for 20 min) was evaluated in rabbit thigh muscle or Vx2 tumors using infusions of microbubbles (Definity, 20 µL/kg) or saline (sham) administered over 5 min. The impact of treatments on drug uptake was assessed with liposomal doxorubicin (Caelyx, 2.5 mg/kg). Applied power levels before and after the injection of microbubbles or saline were compared, and drug uptake was evaluated with fluorometry of tissues harvested 24 hr post-treatment. RESULTS: MRgFUS hyperthermia in muscle and tumors resulted in accurate temperature control (mean =42.0°C, root mean square error (RMSE) = 0.3°C). The power dropped significantly following the injection of microbubbles in muscle and tumors compared to exposures without microbubbles (-21.9% ± 12.5% vs -5.9% ± 7.8%, p = .009 in muscle; -33.8% ± 9.9% vs -3.0% ± 7.2%, p < .001 in tumors). Cavitation monitoring indicated emission of subharmonic, ultraharmonic, and elevated levels of fourth to sixth harmonic frequencies following microbubble injection. The drug delivery was elevated significantly in muscle with the use of microbubble-assisted relative to conventional heating (0.5 ± 0.5 ng/mg vs 0.20 ± 0.04 ng/mg, p = .05), whereas in tumors similar levels were found (11 ± 3 ng/mg vs 16 ± 4 ng/mg, p = .13). CONCLUSIONS: The finding that microbubbles reduce the applied power requirements for hyperthermia has considerable clinical implications. The elevated levels of drug found in muscle but not tumor tissue suggest a complex interplay between the heating effects of microbubbles with those of enhanced permeabilization and possible vascular damage.
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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".