Multimodality imaging‐guided local injection of eccentric magnetic microcapsules with electromagnetically controlled drug release
Bibliographic record
Abstract
BACKGROUND: In the past decade, trackable smart drug delivery systems have played important roles in the treatment of many diseases such as cancer because the drug carriers can be visualized through their distinct physical properties. However, it is still difficult to achieve precise drug delivery because such systems usually rely on a single imaging system. AIM: This study aimed to present a novel type of multimodality imaging-guided strategy to visualize the drug carriers of eccentric magnetic microcapsule (EMM) designed for potential treatment of hepatocellular carcinoma (HCC). METHOD AND RESULTS: nanoparticles are magnetic, with high density and acoustic impedance, allowing for visualization by magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) imaging during local injection. The release of drug from these EMMs can be further controlled by an external electromagnetic field (EMF). As a proof of concept, we demonstrated the process of multimodality imaging to guide local injection and the controlled release of doxorubicin (DOX) from the EMMs in a phantom. We showed that the release rate of DOX was directly correlated to the strength of the EMF. In addition, we cocultured green fluorescent protein (GFP)-transfected HeLa cancer cells with the DOX-loaded EMMs and documented their apoptosis by DOX following the release triggered by EMF. CONCLUSION: The results suggest that these EMMs serve both as contrast agents that can be visualized by multimodality imaging techniques and as smart drug delivery systems, with great potential for precision medicine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".