Bibliographic record
Abstract
Focused ultrasound in combination with circulating microbubbles is being widely investigated as a means to promote spatially targeted drug delivery. This approach has considerable potential in oncology for a range of therapeutic agents. At sufficiently high pressures, above those typically employed in drug delivery, tumor microvessel damage can be induced to an extent that leads to perfusion shutdown and subsequent ischemic tissue necrosis. This approach is often referred to as antivascular ultrasound (AVUS), which has been shown in preclinical work to be capable of enhancing the effects of radiation therapy, antiangiogenic therapy, chemotherapy, and immunotherapy. At present, the mechanisms of AVUS are not well established. It is important to gain a more detailed understanding of the bubble–microvessel interactions that lead to perfusion shutdown, along with the cavitation signatures associated with these behaviors to enable the rational development of effective cavitation based control methods. This talk will provide a brief overview of AVUS therapy in oncology and highlight recent efforts employing two-photon microscopy, high speed optical imaging, and acoustic emission monitoring to gain insights into bubble behavior within small channels and in vivo microvessels under AVUS exposure conditions.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".