Localised drug release using MRI-controlled focused ultrasound hyperthermia
Bibliographic record
Abstract
PURPOSE: Thermosensitive liposomes provide a mechanism for triggering the local release of anticancer drugs, but this technology requires precise temperature control in targeted regions with minimal heating of surrounding tissue. The objective of this study was to evaluate the feasibility of using MRI-controlled focused ultrasound (FUS) and thermosensitive liposomes to achieve thermally mediated localised drug delivery in vivo. MATERIALS AND METHODS: Results are reported from ten rabbits, where a FUS beam was scanned in a circular trajectory to heat 10-15 mm diameter regions in normal thigh to 43°C for 20-30 min. MRI thermometry was used for closed-loop feedback control to achieve temporally and spatially uniform heating. Lyso-thermosensitive liposomal doxorubicin was infused intravenously during hyperthermia. Unabsorbed liposomes were flushed from the vasculature by saline perfusion 2 h later, and tissue samples were harvested from heated and unheated thigh regions. The fluorescence intensity of the homogenised samples was used to calculate the concentration of doxorubicin in tissue. RESULTS: Closed-loop control of FUS heating using MRI thermometry achieved temperature distributions with mean, T90 and T10 of 42.9°C, 41.0°C and 44.8°C, respectively, over a period of 20 min. Doxorubicin concentrations were significantly higher in tissues sampled from heated than unheated regions of normal thigh muscle (8.3 versus 0.5 ng/mg, mean per-animal difference = 7.8 ng/mg, P < 0.05, Wilcoxon matched pairs signed rank test). CONCLUSIONS: The results show the potential of MRI-controlled focused ultrasound hyperthermia for enhanced local drug delivery with temperature-sensitive drug carriers.
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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".