Effect of surface charge and magnetic field on iron oxide nanoparticle permeability in a cell culture model of the blood brain barrier (BBB)
Bibliographic record
Abstract
Surface charge and applied magnetic fields affect cellular uptake of iron oxide nanoparticles (IONPs). However, their effects on permeability of IONPs across the BBB remain unexplored. The aim of this study was to determine the impact of IONP surface charge on permeability in a cell culture model of the BBB in the presence and absence of a magnetic field. Permeability of positively (AmS‐) and negatively charged (TMSPEDT)‐IONPs was evaluated in confluent bEnd.3 monolayers grown on polycarbonate membrane inserts (3 μm pore). Our results show neither IONPs were not transported across the monolayer even in presence of magnetic field. When tight junctions were disrupted using D‐mannitol, 44% and 28% of TMSPEDT‐IONPs were found across the monolayer in presence and absence of magnetic field, respectively. Under the same conditions, there was only 10% permeability of the AmS‐IONPs detected. In conclusion, negatively charged IONPs have a more favorable permeability profile in brain endothelial cells via paracellular route following osmotic disruption. Use of TMSPEDT‐IONPs during transient disruption of BBB, may improve nanoparticle based drug delivery to the brain. Support provided by Manitoba Medical Services Foundation, Thorlakson Foundation and NSERC.
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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".