Magnetic Resonance-Guided High Intensity Focused Ultrasound Generated Hyperthermia: A Feasible Treatment Method in a Murine Rhabdomyosarcoma Model
Bibliographic record
Abstract
Magnetic resonance-guided high intensity focused ultrasound (MRgHIFU) is an established method for producing localized hyperthermia. Given the real-time imaging and acoustic energy modulation, this modality enables precise temperature control within a defined area. Many thermal applications are being explored with this noninvasive, nonionizing technology, such as hyperthermia generation, to release drugs from thermosensitive liposomal carriers. These drugs can include chemotherapies such as doxorubicin, for which targeted release is desired due to the dose-limiting systemic side effects, namely cardiotoxicity. Doxorubicin is a mainstay for treating a variety of malignant tumors and is commonly used in relapsed or recurrent rhabdomyosarcoma (RMS). RMS is the most common solid soft tissue extracranial tumor in children and young adults. Despite aggressive, multimodal therapy, RMS survival rates have remained the same for the past 30 years. To explore a solution for addressing this unmet need, an experimental protocol was developed to evaluate the release of thermosensitive liposomal doxorubicin (TLD) in an immunocompetent, syngeneic RMS mouse model using MRgHIFU as the source of hyperthermia for drug release.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".