Bibliographic record
Abstract
Magnetic tumor targeting with external magnets is a promising method to increase the delivery of cytotoxic agents to tumor cells while reducing side effects. However, this approach suffers from intrinsic limitations, such as the inability to target areas within deep tissues, due mainly to a strong decrease of the magnetic field magnitude away from the magnets. Magnetic resonance navigation (MRN) involving the endovascular steering of therapeutic magnetic microcarriers (TMMC) represents a clinically viable alternative to reach deep tissues. MRN is achieved with an upgraded magnetic resonance imaging (MRI) scanner. In this proof-of-concept preclinical study, the preparation and steering of TMMC which were designed by taking into consideration the constraints of MRN and liver chemoembolization are reported. TMMC were biodegradable microparticles loaded with iron-cobalt nanoparticles and doxorubicin (DOX). These particles displayed high saturation magnetization (Ms = 72 emu g(-1)), MRI tracking compatibility (strong contrast on T2∗-weighted images), appropriate size for the blood vessel embolization (∼50 μm), and sustained release of DOX (over several days). The TMMC were successfully steered in vitro and in vivo in the rabbit model. In vivo targeting of the right or left liver lobes was achieved by MRN through the hepatic artery located 4 cm beneath the skin. Parameters such as flow velocity, TMMC release site in the artery, magnetic gradient and TMMC properties, affected the steering efficiency. These data illustrate the potential of MRN to improve drug targeting in deep tissues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 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) | 0.004 | 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 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".