In situ theranostic platform combining highly localized magnetic fluid hyperthermia, magnetic particle imaging, and thermometry in 3D
Bibliographic record
Abstract
Theranostic platforms, combining diagnostic and therapeutic approaches within one system, have garnered interest in augmenting invasive surgical, chemical, and ionizing interventions.Magnetic particle imaging (MPI) offers a quite recent alternative to established radiation-based diagnostic modalities with its versatile tracer material (superparamagnetic iron oxide nanoparticles, SPION).It also offers a bimodal theranostic framework that can combine tomographic imaging with therapeutic techniques using the very same SPION.Methods: We show the interleaved combination of MPI-based imaging, therapy (highly localized magnetic fluid hyperthermia (MFH)) and therapy safety control (MPI-based thermometry) within one theranostic platform in all three spatial dimensions using a commercial MPI system and a custom-made heating insert.The heating characteristics as well as theranostic applications of the platform were demonstrated by various phantom experiments using commercial SPION.Results: We have shown the feasibility of an MPI-MFH-based theranostic platform by demonstrating high spatial control of the therapeutic target, adequate MPI-based thermometry, and successful in situ interleaved MPI-MFH application.Conclusions: MPI-MFH-based theranostic platforms serve as valuable tools that enable the synergistic integration of diagnostic and therapeutic approaches.The transition into in vivo studies will be essential to further validate their potential, and it holds promising prospects for future advancements.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".