Ultrasound-Enhanced Distribution and Treatment Efficacy of Dox-Loaded Intratumoral In Situ Forming Implants in Murine HCT-15 Tumors
Bibliographic record
Abstract
Local drug delivery systems, such as in situ forming implants (ISFIs), allow for sustained elevated drug concentration directly at the tumor site. However, these systems have been challenging to translate into clinical practice due to poor drug penetration through the implant/tumor boundary. Ultrasound (US) has emerged as a popular approach to enhance drug release and cellular drug uptake from nanoparticles, but little work has been done with its use in combination with ISFIs. In this study, ISFIs were intratumorally injected and treated with therapeutic ultrasound (TUS). A significant (p <;0.05) increase was seen in Doxorubicin (Dox) distribution with ISFIs treated with TUS as compared to ISFIs not treated with TUS. Additionally, the combination of Dox ISFIs with TUS showed a significant (p <;0.01) reduction in tumor growth at 20 days, compared to all other treatment groups. While the mechanism of increased drug distribution and enhanced therapeutic efficacy is not yet clear, hyperthermia was seen with all groups that involved application of TUS for these US exposure parameters. This could be attributed to elevated drug penetration and synergy with the chemotherapy, but additional experiments need to confirm this hypothesis. This study demonstrates, for the first time, US-enhanced drug distribution from ISFIs. The combination of US with local chemotherapy could eventually facilitate translation of local drug delivery systems into clinical practice.
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 | 0.000 |
| 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".