Modeling and simulation of smart magnetic self-assembled nanomicelle trajectories in an internal thoracic artery flow for breast cancer therapy
Bibliographic record
Abstract
Abstract Magnetic drug targeting (MDT) is one of the most modern techniques in cancer therapy for its ability to reduce the side effects of chemotherapy experienced by systemic administration. In this study, a comprehensive mathematical model has been developed to predict the drug particle trajectories of anticancer dasatinib magnetic nanomicelles (DAS-MNM) released in an internal thoracic artery (ITA) blood flow for breast cancer therapy. Several factors are investigated in regard to the efficiency of MDT through the ITA, including magnetic field strength (MFS), relative magnetic permeability, magnet size, drug particle size, and, initial position of drug particle. The drug particle trajectory results confirmed the successful MDT using an external magnetic field with a capture efficiency of more than 90%, through the accumulation of a wide range of particle sizes of DAS-MNM close to the external magnetic field source at the arterial wall than in other positions. Moreover, the results showed that the number of trapped particles increased with increasing both MFS and drug particle diameter within the target tissue, while the drug particle permeability does not have a considerable effect on the particle retention. In addition, for achieving a successful drug/cargo delivery through the arteries, the magnetic field, the particle size, and initial release locations should be adjusted simultaneously. The present work offers insights into the critical factors in MDT with a significant impact on breast cancer therapy, tissue engineering, and regenerative medicine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".