Simulation of Enhanced Deposition Due to Magnetic Field Alignment of Ellipsoidal Particles in a Lung Bifurcation
Bibliographic record
Abstract
BACKGROUND: Aerosolized chemotherapy has been recognized as a potential treatment for lung cancer. The challenge of providing sufficient therapeutic effects without reaching dose-limiting toxicity levels hinders the development of aerosolized chemotherapy. This could be mitigated by increasing drug-delivery efficiency with a noninvasive drug-targeting delivery method. The purpose of this study is to use direct numerical simulations to study the resulting local enhancement of deposition due to magnetic field alignment of high aspect ratio particles. METHODS: High aspect ratio particles were approximated by a rigid ellipsoid with a minor diameter of 0.5 μm and fluid particle density ratio of 1,000. Particle trajectories were calculated by solving the coupled fluid particle equations using an in-house micro-macro grid finite element algorithm based on a previously developed fictitious domain approach. Particle trajectories were simulated in a morphologically realistic geometry modeling a symmetrical terminal bronchiole bifurcation. Flow conditions were steady inspiratory air flow due to typical breathing at 18 L/min. Deposition efficiency was estimated for two different cases: [1] particles aligned with the streamlines and [2] particles with fixed angular orientation simulating the magnetic field alignment of our previous in vitro study. RESULTS: The local enhancement factor defined as the ratio between deposition efficiency of Case [1] and Case [2] was found to be 1.43 and 3.46 for particles with an aspect ratio of 6 and 20, respectively. CONCLUSIONS: Results indicate that externally forcing local alignment of high aspect ratio particles can increase local deposition considerably.
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 | 0.001 | 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 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".