Hyperthermia-mediated doxorubicin release from thermosensitive liposomes using MR-HIFU: Therapeutic effect in rabbit Vx2 tumours
Bibliographic record
Abstract
PURPOSE: The aim of this study was to determine whether localised drug release using thermosensitive liposomal doxorubicin (TLD) and mild hyperthermia produced by a clinical magnetic resonance high intensity focused ultrasound (MR-HIFU) system improves anti-tumour efficacy over TLD alone in rabbit Vx2 tumours. MATERIALS AND METHODS: Rabbits bearing one Vx2 thigh tumour (n = 6 per group) were administered TLD (1.67 mg/kg) either with or without MR-HIFU mild hyperthermia (20 min, 42.0 °C). Tumour progression was measured using contrast-enhanced T1-weighted MR imaging. Toxicity was evaluated by changes in body weight, blood counts, and blood chemistry. Tumour volume, body weight, and blood data were acquired weekly for the first month and biweekly thereafter. RESULTS: Rabbits treated with TLD plus MR-HIFU mild hyperthermia had target region temperatures with spatial-median, temporal-mean of 41.4° ± 0.6 °C; 10th and 90th percentile temperatures were 40.2 and 42.7 °C. All six rabbits that received TLD alone had rapid tumour progression and reached the tumour size end point (maximum dimension >6 cm) within 24 days. Four of six rabbits treated with TLD plus MR-HIFU mild hyperthermia survived to the study end point of 60 days; one reached tumour size end point, one had hyperthermia-related toxicity, all had at least a transient decrease in tumour volume. Weekly body weight, complete blood counts, and blood chemistry did not reveal additional evidence of drug or hyperthermia-related toxicity. CONCLUSIONS: Rabbit Vx2 tumours treated with a single infusion of TLD during MR-HIFU mild hyperthermia had reduced tumour growth vs. tumours treated with TLD alone. These findings are an important step toward clinical translation of localised drug delivery using MR-HIFU and TLD.
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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) | 0.000 | 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".