Using ultrasound and microbubble to enhance the effects of conventional cancer therapies in clinical settings
Bibliographic record
Abstract
It has been demonstrated in preclinical research that the administration of microbubbles with ultrasound can augment the proapoptotic sphingolipid pathway and enhance chemotherapy or radiation therapy-induced vascular endothelial disruption resulting in enhanced tumor cell death. Specifically, ultrasound-stimulated microbubbles (USMB) can increase blood vessel permeability facilitating the release of therapeutic substances in the target area. USMB can also serve as a potential radiation enhancing therapy as USMB exposure increases tumor cell death significantly as observed in preclinical models. Clinical studies have found the combination of USMB and these existing cancer therapies to be safe and also to be associated with greater tumor responses. USMB-based treatment can be applicable in a clinical setting using either ultrasound imaging or magnetic resonance imaging (MRI) guidance for precise treatment. In the latter, the ultrasound device is integrated into the MRI system platform for sonication to facilitate microbubble stimulation. In this review, we concisely present findings related to USMB and existing cancer therapies (chemotherapy and radiation therapy) in clinical trial settings. The possible underlying mechanism involved in USMB-enhanced chemotherapy or radiotherapy enhancement is also discussed. Lastly, the study concludes with some limitations and an examination of the future direction of these combined therapies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".