Efficient ultrasound-mediated drug delivery to orthotopic liver tumors – Direct comparison of doxorubicin-loaded nanobubbles and microbubbles
Bibliographic record
Abstract
Abstract Liver metastasis is a major obstacle in treating aggressive cancers, and current therapeutic options often prove insufficient. To overcome these challenges, there has been growing interest in ultrasound-mediated drug delivery using lipid-shelled microbubbles (MBs) and nanobubbles (NBs) as promising strategies for enhancing drug delivery to tumors. Our previous work demonstrated the potential of Doxorubicin-loaded C 3 F 8 NBs (hDox-NB, 280 ± 123 nm) in improving cancer treatment in vitro using low-frequency ultrasound. In this study, we investigated the pharmacokinetics and biodistribution of sonicated hDox-NBs in orthotopic rat liver tumors. We compared their delivery and therapeutic efficiency with size-isolated MBs (hDox-MB, 1104 ± 373 nm). Results showed a similar accumulation of hDox in tumors treated with hDox-MBs and unfocused therapeutic ultrasound (hDox-MB+TUS) and hDox-NB+TUS. However, significantly increased apoptotic cell death in the tumor and fewer off-target apoptotic cells in the normal liver were found upon the treatment with hDox-NB+TUS. The tumor-to-liver apoptotic ratio was elevated 9.4-fold following treatment with hDox-NB+TUS compared to hDox-MB+TUS, suggesting that the therapeutic efficacy and specificity are significantly increased when using hDox-NB+TUS. These findings highlight the potential of this approach as a viable treatment modality for liver tumors. By elucidating the behavior of drug-loaded bubbles in vivo , we aim to contribute to developing more effective liver cancer treatments that could ultimately improve patient outcomes and decrease off-target side effects.
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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".