Trafficking through the blood–brain barrier is directed by core and outer surface components of layer‐by‐layer nanoparticles
Bibliographic record
Abstract
Drug-carrying nanoparticles are a promising strategy to deliver therapeutics into the brain, but their translation requires better characterization of interactions between nanomaterials and endothelial cells of the blood-brain barrier (BBB). Here, we use a library of 18 layer-by-layer electrostatically assembled nanoparticles (NPs) to independently assess the impact of NP core and surface materials on in vitro uptake, transport, and intracellular trafficking in brain endothelial cells. We demonstrate that NP core stiffness determines the magnitude of transport, while surface chemistry directs intracellular trafficking. Finally, we demonstrate that these factors similarly dictate in vivo BBB transport using intravital imaging through cranial windows in mice. We identify that hyaluronic acid surface chemistry increases transport across the BBB in vivo, and flow conditions are necessary to replicate this finding in vitro. Taken together, these findings highlight the importance of assay geometry, cell biology, and fluid flow in developing nanocarriers for 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".