Deep Tissue Penetration of Bottle-Brush Polymers via Cell Capture Evasion and Fast Diffusion
Bibliographic record
Abstract
Drug nanocarriers (NCs) capable of crossing the vascular endothelium and deeply penetrating into dense tissues of the CNS could potentially transform the management of neurological diseases. In the present study, we investigated the interaction of bottle-brush (BB) polymers with different biological barriers in vitro and in vivo and compared it to nanospheres of similar composition. In vitro internalization and permeability assays revealed that BB polymers are not internalized by brain-associated cell lines and translocate much faster across a blood–brain barrier model compared to nanospheres of similar hydrodynamic diameter. These observations performed under static, no-flow conditions were complemented by dynamic assays performed in microvessel arrays on chip and confirmed that BB polymers can escape the vasculature compartment via a paracellular route. BB polymers injected in mice and zebrafish larvae exhibit higher penetration in brain tissues and faster extravasation of microvessels located in the brain compared to nanospheres of similar sizes. The superior diffusivity of BBs in extracellular matrix-like gels combined with their ability to efficiently cross endothelial barriers via a paracellular route position them as promising drug carriers to translocate across the blood–brain barrier and penetrate dense tissue such as the brain, two unmet challenges and ultimate frontiers in nanomedicine.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".