Using ultrasound and microbubble to enhance the effects of conventional cancer therapies in clinical settings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 it