Magnetic aerosol drug targeting in lung cancer therapy using permanent magnet
Bibliographic record
Abstract
Primary bronchial cancer accounts for almost 20% of all cancer death worldwide. One of the emerging techniques with tremendous power for lung cancer therapy is magnetic aerosol drug targeting (MADT). The use of a permanent magnet for effective drug delivery in a desired location throughout the lung requires extensive optimization, but it has not been addressed yet. In the present study, the possibility of using a permanent magnet for trapping the particles on a lung tumor is evaluated numerically in the Weibel's model from G0 to G3. The effect of different parameters is considered on the efficiency of particle deposition in a tumor located on a distant position of the lung bronchi and bronchioles. Also, the effective position of the magnetic source, tumor size, and location are the objectives for particle deposition. The results show that a limited particle deposition occurs on the lung branches in passive targeting. However, the incorporation of a permanent magnet next to the tumor enhanced the particle deposition fraction on G2 to up to 49% for the particles of 7 µm diameter. Optimizing the magnet size could also improve the particle deposition fraction by 68%. It was also shown that the utilization of MADT is essential for effective drug delivery to the tumors located on the lower wall of airway branches given the dominance of the air velocity and resultant drag force in this region. The results demonstrated the high competence and necessity of MADT as a noninvasive drug delivery method for lung cancer therapy.
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 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".