Bibliographic record
Abstract
A new method for the navigation of therapeutic agents in the vascular network is introduced. This method, dubbed Dipole Field Navigation (DFN), is characterized by high directional gradients and a high magnetic field strength. The latter is used to bring magnetic therapeutic agents at saturation magnetization such that when combined with high directional gradients, effective navigation at any depths within the patient can be achieved. DFN does not have many of the constraints of gradient coil-based platforms, which include potential peripheral nerve stimulations, reduced directional changes and slew rates of the gradient fields, overheating of the coils, and high implementation cost. To achieve such specifications, soft ferromagnetic cores are positioned at specific locations inside the tunnel of a clinical MRI scanner providing a high uniform field of typically up to 3T, sufficient to bring both the cores and the therapeutic agents at full saturation magnetization. The field distortions created by the cores result in gradients exceeding 300 mT/m for whole body interventions. Hence, with such cores placed at specific locations, the resulting gradients would cause the therapeutic agents to follow a precise path in the vascular network towards the targeted region. In this paper, the fundamental theory of DFN with preliminary in vitro experimental results using one core in a 1.5T scanner confirms the potential of DFN for targeted drug delivery.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".