Intravital imaging and cavitation monitoring of antivascular ultrasound in tumor microvasculature
Bibliographic record
Abstract
Rationale: Focused ultrasound-stimulated microbubbles have been shown to be capable of inducing blood flow shutdown and necrosis in a range of tissue types in an approach termed antivascular ultrasound or nonthermal ablation.In oncology, this approach has demonstrated tumor growth inhibition, and profound synergistic antitumor effects when combined with traditional platforms of chemo-, radiation-and immune-therapies.However, the exposure schemes employed have been broad and underlying mechanisms remain unclear with fundamental questions about exposures, vessel types and sizes involved, and the nature of bubble behaviors and their acoustic emissions resulting in vascular damage -impeding the establishment of standard protocols.Methods: Here, ultrasound transmitters and receivers are integrated into a murine dorsal window chamber tumor model for intravital microscopy studies capable of real-time visual and acoustic monitoring during antivascular ultrasound.Vessel type (normal and tumor-affected), caliber, and viability are assessed under higher pressure conditions (1, 2, and 3 MPa), and cavitation signatures are linked to the biological effects.Results: Vascular events occurred preferentially in tumor-affected vessels with greater incidence in smaller vessels and with more severity as a function of increasing pressure.Vascular blood flow shutdown was found to be due to a combination of focal disruption events and network-related flow changes.Acoustic emissions displayed elevated broadband noise and distinct sub-and ultra-harmonics and their associated third-order peaks with increasing pressure. Conclusions:The observed vascular events taken collectively with identified cavitation signatures provide an improved mechanistic understanding of antivascular ultrasound at the microscale, with implications for establishing a specific treatment protocol and control platform.
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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".