Synchronous Intravital Imaging and Cavitation Monitoring of Antivascular Focused Ultrasound in Tumor Microvasculature Using Monodisperse Low Boiling Point Nanodroplets
Bibliographic record
Abstract
Focused ultrasound-stimulated microbubbles can induce blood flow shutdown and ischemic necrosis at higher pressures in an approach termed antivascular ultrasound. Combined with conventional therapies of chemotherapy, immunotherapy, and radiation therapy, this approach has demonstrated tumor growth inhibition and profound synergistic antitumor effects. However, the lower cavitation threshold of microbubbles can potentially yield off-target damage that the polydispersity of clinical agent may further exacerbate. Here we investigate the use of a monodisperse nanodroplet formulation for achieving antivascular effects in tumors. We first develop stable low boiling point monodisperse lipid nanodroplets and examine them as an alternative agent to mediate antivascular ultrasound. With synchronous intravital imaging and ultrasound monitoring of focused ultrasound-stimulated nanodroplets in tumor microvasculature, we show that nanodroplets can trigger blood flow shutdown and do so with a sharper pressure threshold and with fewer additional events than conventionally used microbubbles. We further leverage the smaller size and prolonged pharmacokinetic profile of nanodroplets to allow for potential passive accumulation in tumor tissue prior to antivascular ultrasound, which may be a means by which to promote selective tumor targeting. We find that vascular shutdown is accompanied by inertial cavitation and complex-order sub- and ultraharmonic acoustic signatures, presenting an opportunity for effective feedback control of antivascular ultrasound.
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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".