Preclinical dosimetry of magnetic fluid hyperthermia for bladder cancer
Bibliographic record
Abstract
BACKGROUND: Despite positive efficacy, thermotherapy is not widely used in clinical oncology. Difficulties associated with field penetration and controlling power deposition patterns in heterogeneous tissue have limited its use for heating deep in the body. Heat generation using iron-oxide super-paramagnetic nanoparticles excited with magnetic fields has been demonstrated to overcome some of these limitations. The objective of this preclinical study is to investigate the feasibility of treating bladder cancer with magnetic fluid hyperthermia (MFH) by analyzing the thermal dosimetry of nanoparticle heating in a rat bladder model. METHODS: The bladders of 25 female rats were injected with 0.4 ml of Actium Biosystems magnetite-based nanoparticles (Actium Biosystems, Boulder CO) via catheters inserted in the urethra. To assess the distribution of nanoparticles in the rat after injection we used the 7 T small animal MRI system (Bruker ClinScan, Bruker BioSpin MRI GmbH, Ettlingen, Germany). Heat treatments were performed with a small animal magnetic field applicator (Actium Biosystems, Boulder CO) with a goal of raising bladder temperature to 42°C in <10min and maintaining for 60min. Temperatures were measured throughout the rat with seven fiberoptic temperature probes (OpSens Technologies, Quebec Canada) to characterize our ability to localize heat within the bladder target. RESULTS: The MRI study confirms the effectiveness of the catheterization procedure to homogenously distribute nanoparticles throughout the bladder. Thermal dosimetry data demonstrate our ability to controllably raise temperature of rat bladder ≥1°C/min to a steady-state of 42°C. CONCLUSION: Our data demonstrate that a MFH system provides well-localized heating of rat bladder with effective control of temperature in the bladder and minimal heating of surrounding tissues.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".