Magnetic nanoparticle-promoted droplet vaporization for in vivo stimuli-responsive cancer theranostics
Bibliographic record
Abstract
The development of efficient strategies for in vivo stimuli-responsive cancer treatment and personalized biomedicine is a great challenge. To overcome the critical issues and limitations of traditional protocols using acoustic droplet vaporization and optical droplet vaporization in stimuli-responsive tumor treatment, we herein report a new strategy, magnetic droplet vaporization (MDV), based on nanobiotechnology, for efficient magnetic field-responsive cancer theranostics. Perfluorohexane (PFH)-encapsulated superparamagnetic hollow iron oxide nanoparticles with a high magnetic-thermal energy transfer capability quickly respond to an external alternating current (a.c.) magnetic field to produce thermal energy and raise the temperature of the surrounding tumor tissue. The encapsulated PFH, with a desirable boiling point of ~56°C, can be vaporized to enhance the performance of ultrasound imaging of tumors, as systematically demonstrated both in vitro and in vivo. The magnetic–thermal energy transfer further ablated and removed tumors in mice tumor xenograft models. This unique MDV principle with high versatility and performance is expected to broaden the biomedical applications of nanotechnology and promote clinical translations of intelligent diagnostic and therapeutic modalities, especially for battling cancer.
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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) | 0.002 | 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".