Investigations into the use of MRI-controlled focused ultrasound for hyperthermia-mediated drug delivery
Bibliographic record
Abstract
Combining localized heating with thermosensitive liposomes provides a means for triggering the rapid release of active drug in a targeted region. The objective of this research was to demonstrate the feasibility of using MRI-controlled focused ultrasound hyperthermia in rabbit VX2 tumors and bone to achieve thermally-mediated localized drug delivery. To generate a uniform region of mild heating, a focused ultrasound transducer was mechanically scanned in a circular trajectory by an MRI-compatible positioner. MRI temperature images were continuously acquired during scanned heating, and applied power was adjusted in a multi-point feedbackcontrol loop based on temperatures measured in tumors or in soft tissue adjacent to bone. Lysothermosensitive liposomal doxorubicin was infused intravenously during hyperthermia. Two hours post-treatment, unabsorbed liposomes were flushed from the vasculature with saline. Drug concentrations in homogenized tissues sampled from heated and unheated regions were measured by doxorubicin fluorescence. MRI-controlled focused ultrasound achieved stable heating at 43°C for 20 minutes in VX2 tumors implanted in the thighs of 5 rabbits, and at the muscle-bone interface of 9 rabbits. Localized heating resulted in a 20-fold increase in doxorubicin concentration localized to heated tumors, with 8 and 17-fold increases in bone marrow and muscle adjacent to the heated bone interface.
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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 | 0.001 | 0.001 |
| 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".