Simulation of Remote Forces Generated by High-Temperature Superconducting Bulks for Magnetic Drug Delivery
Bibliographic record
Abstract
The strong background fields and field gradients required for the magnetic delivery of chemotherapeutic drugs in cancer treatments has proven to be challenging to achieve at the scale of the human body. Although several magnetic drug delivery (MDD) methods have been proposed to generate adequate forces in deep tissues, current technologies lack in either force strength, directional changes, and/or duty cycle of the treatment. The current MDD system is capable of generating the strongest forces, dipole field navigation, uses ferromagnetic (FM) cores in the strong uniform field of an MRI to steer the drug-loaded particles. Hence, considering the nearly ten-fold increase in magnetization of high-temperature superconducting (HTS) bulks in comparison to the strongest FM, the forces obtained in dipole field navigation could potentially be increased by replacing the FM cores with HTS bulks. In this work, we evaluate two different methods for generating magnetic forces using an HTS bulk in a magnetic resonance imaging (MRI) scanner by using finite element method simulations. First, the HTS pellet is zero field cooled and inserted in the uniform field of an MRI, such that the pellet is magnetized by the fringe field of the MRI. Second, a field cooled HTS pellet is rotated in the uniform field of an MRI in order to generate magnetic field gradients. The magnetic forces produced by both methods are compared with those obtained by dipole field navigation. In both methods evaluated, we found that the forces generated by the HTS pellet are either complementary to or stronger than those obtained with FM cores. Therefore, this work shows that HTS bulks could potentially be used to navigate magnetic particles in the deep tissues of the human vascular network more efficiently.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".