Impact of Focused Ultrasound-enhanced Drug Delivery on Survival in Rats with Glioma
Bibliographic record
Abstract
Malignancies of the brain remain difficult to treat with chemotherapy because the selective permeability of the blood‐brain barrier (BBB) blocks many potent agents from reaching their target. Previous studies have illustrated the feasibility of drug and antibody delivery across the BBB using MRI‐guided focused ultrasound. In this study, we investigated the impact of focused ultrasound‐enhanced delivery of doxorubicin on survival in rats with aggressive glioma. Sprague‐Dawley rats were implanted with 9 L gliosarcoma cells in the brain. Eight days after implantation, each rat received one of the following: (1) no treatment (control), (2) a single treatment with microbubble‐enhanced MRI‐guided focused ultrasound (FUS only), (3) a single treatment with i.v. liposomal doxorubicin (DOX only), or (4) a single treatment with microbubble‐enhanced MRI‐guided focused ultrasound and concurrent i.v. injections of liposomal doxorubicin (FUS+DOX). The survival time from implantation to death or euthanasia was recorded. We observed a modest but significant increase in median survival time in rats treated with combined MRI‐guided focused ultrasound chemotherapy, compared to chemotherapy alone (p<0.001). There was no significant improvement in survival between those who received stand‐alone chemotherapy and those who did not receive any treatment (p>0.10). Our study demonstrates for the first time a therapeutic benefit achieved with ultrasound‐enhanced drug delivery across the blood‐brain barrier. This confirmation of efficacy in an in vivo tumor model indicates that targeted drug delivery using MRI‐guided focused ultrasound has the potential to have a major impact on the treatment of patients with brain tumors and other neurological disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".