Ultrasound-triggered oxygen-loaded nanodroplets enhance and monitor cerebral damage from sonodynamic therapy
Bibliographic record
Abstract
In sonodynamic therapy, cellular toxicity from sonosensitizer drugs, such as 5-aminolevulinic acid hydrochloride (5-ALA), may be triggered with focused ultrasound through the production of reactive oxygen species (ROS). Here we show that by increasing local oxygen during treatment, using oxygen-loaded perfluorocarbon nanodroplets (250 +/- 8 nm), we can increase the damage induced by 5-ALA, and monitor the severity by recording acoustic emissions in the brain. To achieve this, we sonicated the right striatum of 16 healthy rats after an intravenous dose of 5-ALA (200 mg/kg), followed by saline, nanodroplets, or oxygen-loaded nanodroplets. We assessed haemorrhage, edema and cell apoptosis immediately following, 24 hr, and 48 hr after focused ultrasound treatment. The localized volume of damaged tissue was significantly enhanced by the presence of oxygen-loaded nanodroplets, compared to ultrasound with unloaded nanodroplets (3-fold increase), and ultrasound alone (40-fold increase). Sonicating 1 hr following 5-ALA injection was found to be more potent than 2 hr following 5-ALA injection (2-fold increase), and the severity of tissue damage corresponded to the acoustic emissions from droplet vaporization. Enhancing the local damage from 5-ALA with monitored cavitation activity and additional oxygen could have significant implications in the treatment of atherosclerosis and non-invasive ablative surgeries.
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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) | 0.000 | 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".