An artificially engineered “tumor bio-magnet” for collecting blood-circulating nanoparticles and magnetic hyperthermia
Bibliographic record
Abstract
It is a great challenge to directly endow a tumor with specific functions for theranostic treatment. In this study, we report on a novel approach to transform a tumor into a "bio-magnet", to be magnetized on demand, in order to create an intrinsic tumor magnetic field that would collect magnetic nanoparticles (MNPs) circulating in the blood and achieve simultaneous magnetic hyperthermia. This was achieved by the localized intratumoral injection of liquid Nd2Fe14B/Fe3O4-PLGA, followed by solvent exchange that induces a liquid-to-solid transformation. After the magnetism charging process, the solid Nd2Fe14B/Fe3O4-PLGA implant was endowed with permanent magnetic properties and in situ created the magnetic field within the tumor tissue, making the tumor a "bio-magnet". After the creation of the bio-magnet, intravenously injected MNPs accumulated into the tumor tissue due to the tumor magnetic field. Importantly, both the in vitro and ex vivo results demonstrated the high efficiency of the implanted bio-magnet for magnetic hyperthermia. This new approach achieves magnetic targeting by creating a tumor "bio-magnet", which generates a strong magnetic field within the tumor, paving a new way for the development of an efficient targeting strategy for tumor therapy.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".