Enhancing drug delivery for boron neutron capture therapy of brain tumors with focused ultrasound
Bibliographic record
Abstract
BACKGROUND: Glioblastoma is a notoriously difficult tumor to treat because of its relative sanctuary in the brain and infiltrative behavior. Therapies need to penetrate the CNS but avoid collateral tissue injury. Boron neutron capture therapy (BNCT) is a treatment whereby a (10)B-containing drug preferentially accumulates in malignant cells and causes highly localized damage when exposed to epithermal neutron irradiation. Studies have suggested that (10)B-enriched L-4-boronophenylalanine-fructose (BPA-f) complex uptake can be improved by enhancing the permeability of the cerebrovasculature with osmotic agents. We investigated the use of MRI-guided focused ultrasound, in combination with injectable microbubbles, to noninvasively and focally augment the uptake of BPA-f. METHODS: With the use of a 9L gliosarcoma tumor model in Fisher 344 rats, the blood-brain and blood-tumor barriers were disrupted with pulsed ultrasound using a 558 kHz transducer and Definity microbubbles, and BPA-f (250 mg/kg) was delivered intravenously over 2 h. (10)B concentrations were estimated with imaging mass spectrometry and inductively coupled plasma atomic emission spectroscopy. RESULTS: The tumor to brain ratio of (10)B was 6.7 ± 0.5 with focused ultrasound and only 4.1 ± 0.4 in the control group (P < .01), corresponding to a mean tumor [(10)B] of 123 ± 25 ppm and 85 ± 29 ppm, respectively. (10)B uptake in infiltrating clusters treated with ultrasound was 0.86 ± 0.10 times the main tumor concentration, compared with only 0.29 ± 0.08 in controls. CONCLUSIONS: Ultrasound increases the accumulation of (10)B in the main tumor and infiltrating cells. These findings, in combination with the expanding clinical use of focused ultrasound, may offer improvements in BNCT and the treatment of glioblastoma.
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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".