Focused Ultrasound and Microbubbles-Mediated Drug Delivery to Brain Tumor
Bibliographic record
Abstract
The presence of blood-brain barrier (BBB) and/or blood-brain-tumor barriers (BBTB) is one of the main obstacles to effectively deliver therapeutics to our central nervous system (CNS); hence, the outcomes following treatment of malignant brain tumors remain unsatisfactory. Although some approaches regarding BBB disruption or drug modifications have been explored, none of them reach the criteria of success. Convention-enhanced delivery (CED) directly infuses drugs to the brain tumor and surrounding tumor infiltrating area over a long period of time using special catheters. Focused ultrasound (FUS) now provides a non-invasive method to achieve this goal via combining with systemically circulating microbubbles to locally enhance the vascular permeability. In this review, different approaches of delivering therapeutic agents to the brain tumors will be discussed as well as the characterization of BBB and BBTB. We also highlight the mechanism of FUS-induced BBB modulation and the current progress of this technology in both pre-clinical and clinical studies.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".