Application of Ultrasound Combined with Microbubbles for Cancer Therapy
Bibliographic record
Abstract
At present, cancer is one of the leading causes of death worldwide. Treatment failure remains one of the prime hurdles in cancer treatment due to the metastatic nature of cancer. Techniques have been developed to hinder the growth of tumours or at least to stop the metastasis process. In recent years, ultrasound therapy combined with microbubbles has gained immense success in cancer treatment. Ultrasound-stimulated microbubbles (USMB) combined with other cancer treatments including radiation therapy, chemotherapy or immunotherapy has demonstrated potential improved outcomes in various in vitro and in vivo studies. Studies have shown that low dose radiation administered with USMB can have similar effects as high dose radiation therapy. In addition, the use of USMB in conjunction with radiotherapy or chemotherapy can minimize the toxicity of high dose radiation or chemotherapeutic drugs, respectively. In this review, we discuss the biophysical properties of USMB treatment and its applicability in cancer therapy. In particular, we highlight important preclinical and early clinical findings that demonstrate the antitumour effect combining USMB and other cancer treatment modalities (radiotherapy and chemotherapy). Our review mainly focuses on the tumour vascular effects mediated by USMB and these cancer therapies. We also discuss several current limitations, in addition to ongoing and future efforts for applying USMB in cancer treatment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".