Repeated 5-aminolevulinic acid mediated sonodynamic therapy using magnetic resonance guided focused ultrasound in rat brain tumour models
Bibliographic record
Abstract
Sonodynamic therapy is an emerging therapeutic approach against brain tumours. However, the treatment scheme and ultrasound parameters have yet to be explored for clinical translation. Our study aimed to optimize ultrasound parameters for sonodynamic therapy (SDT) with 5-ALA as a sonosensitizing agent and to evaluate its therapeutic outcome on the rodent 9L gliosarcoma and the human U87 glioblastoma models. We stereotactically implanted brain tumour cells in rats and monitored tumour volume via MRI. SDT was conducted weekly using a 60 mg/kg dose of 5-ALA, injected intravenously 6 h before sonication. We used a driving frequency of 580 kHz with 0.75 MPa and evaluated the effect of different burst lengths to optimize ultrasound parameters. We also tested SDT against advanced-stage brain tumours to verify its efficacy further. Our results showed that a longer burst length could improve therapeutic outcomes. Tumour growth inhibition was established only in the first three weeks with 10 ms and 50 ms burst length sonication, but 86 ms burst length greatly improved the survival outcome. Therefore, the therapeutic efficacy is proportionate to the burst length and, thus, the total delivered energy. Repeated SDT using multiple targets to cover the entire tumour volume with optimal ultrasound parameters can achieve significant anti-tumour effects in both 9L and U87 models. Lastly, our results on late-stage tumour treatments showed that SDT can still provide prolonged survival. These promising findings demonstrate that repeated SDT using transcranial-focused ultrasound together with 5-ALA can optimize anti-tumour effects and even lead to complete clearance of the tumours. This weekly treatment with pulsed ultrasound sonication strategy is practical for future clinical translation.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".