Acoustically Stimulated Microbubbles Enhance Radiotherapy in Large Prostate Tumours: In Vivo Characterization of Acute Tissue and Vascular Response
Bibliographic record
Abstract
This study investigates the therapeutic synergy of acoustically stimulated microbubbles (MB) with radiation therapy (XRT) in treating large prostate tumours using an in vivo xenograft model. MB were administered intravenously and activated with focused ultrasound (FUS) before radiation exposure. The ArrayUS Clinical FUS System was utilized to effectively stimulate MB directly within the tumour microvasculature, demonstrating reliable tumour sensitization and XRT enhancement. Histological and immunohistochemical analyses revealed increased tumour cell apoptosis, vascular disruption, and endothelial injury in the combined MB+FUS+XRT group compared to XRT alone. Power Doppler imaging further demonstrated acute reductions in tumour perfusion, consistent with microvascular collapse. Mechanistically, this response was associated with activation of the acid sphingomyelinase (ASMase)/ceramide pathway. Overall, the findings support the clinical potential of MB-enhanced FUS as a non-invasive adjunct to radiotherapy, offering a promising strategy for improving local tumour control and therapeutic outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".