Internalization of targeted quantum dots by brain capillary endothelial cells in vivo
Bibliographic record
Abstract
Receptors located on brain capillary endothelial cells forming the blood-brain barrier are the target of most brain drug delivery approaches. Yet, direct subcellular evidence of vectorized transport of nanoformulations into the brain is lacking. To resolve this question, quantum dots were conjugated to monoclonal antibodies (Ri7) targeting the murine transferrin receptor. Specific transferrin receptor-mediated endocytosis of Ri7-quantum dots was first confirmed in N2A and bEnd5 cells. After intravenous injection in mice, Ri7-quantum dots exhibited a fourfold higher volume of distribution in brain tissues, compared to controls. Immunofluorescence analysis showed that Ri7-quantum dots were sequestered throughout the cerebral vasculature 30 min, 1 h, and 4 h post injection, with a decline of signal intensity after 24 h. Transmission electron microscopic studies confirmed that Ri7-quantum dots were massively internalized by brain capillary endothelial cells, averaging 37 ± 4 Ri7-quantum dots/cell 1 h after injection. Most quantum dots within brain capillary endothelial cells were observed in small vesicles (58%), with a smaller proportion detected in tubular structures or in multivesicular bodies. Parenchymal penetration of Ri7-quantum dots was extremely low and comparable to control IgG. Our results show that systemically administered Ri7-quantum dots complexes undergo extensive endocytosis by brain capillary endothelial cells and open the door for novel therapeutic approaches based on brain endothelial cell drug delivery.
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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) | 0.001 | 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".