Magnetic field enhanced convective diffusion of iron oxide nanoparticles in an osmotically disrupted cell culture model of the blood–brain barrier
Bibliographic record
Abstract
PURPOSE: The present study examines the use of an external magnetic field in combination with the disruption of tight junctions to enhance the permeability of iron oxide nanoparticles (IONPs) across an in vitro model of the blood-brain barrier (BBB). The feasibility of such an approach, termed magnetic field enhanced convective diffusion (MFECD), along with the effect of IONP surface charge on permeability, was examined. METHODS: The effect of magnetic field on the permeability of positively (aminosilane-coated [AmS]-IONPs) and negatively (N-(trimethoxysilylpropyl)ethylenediaminetriacetate [EDT]-IONPs) charged IONPs was evaluated in confluent monolayers of mouse brain endothelial cells under normal and osmotically disrupted conditions. RESULTS: Neither IONP formulation was permeable across an intact cell monolayer. However, when tight junctions were disrupted using D-mannitol, flux of EDT-IONPs across the bEnd.3 monolayers was 28%, increasing to 44% when a magnetic field was present. In contrast, the permeability of AmS-IONPs after osmotic disruption was less than 5%. The cellular uptake profile of both IONPs was not altered by the presence of mannitol. CONCLUSIONS: MFECD improved the permeability of EDT-IONPs through the paracellular route. The MFECD approach favors negatively charged IONPs that have low affinity for the brain endothelial cells and high colloidal stability. This suggests that MFECD may improve IONP-based drug delivery to the brain.
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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 | 0.000 |
| 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".