Copper Transport Mediated by Nanocarrier Systems in a Blood–Brain Barrier In Vitro Model
Bibliographic record
Abstract
Copper (Cu) is a cofactor of various metalloenzymes and has a role in neurodegenerative diseases with disturbed Cu homeostasis, for example, in Alzheimer's disease (AD) and Menkes disease. To address Cu imbalances, we synthesized two different dendritic nanoparticles (NP) for the transport of Cu(II) ions across the blood-brain barrier (BBB). The synthesized NPs show low toxicity and high water solubility and can stabilize high amounts of Cu(II). The Cu(II)-laden NPs crossed cellular membranes and increased the cellular Cu level. A human brain microvascular endothelial cell (HBMEC) model was established to investigate the permeability of the NPs through the BBB. By comparing the permeability × surface area product (PSe) of reference substances with those of NPs, we observed that NPs crossed the BBB model two times more effectively than (14)C-sucrose and sodium fluorescein (NaFl) and up to 60× better than Evans Blue labeled albumin (EBA). Our results clearly indicate that NPs cross the BBB model effectively. Furthermore, Cu was shielded by the NPs, which decreased the Cu toxicity. The novel design of the core-shell NP enabled the complexation of Cu(II) in the outer shell and therefore facilitated the pH-dependent release of Cu in contrast to core-multishell NPs, where the Cu(II) ions are encapsulated in the core. This allows a release of Cu into the cytoplasm. In addition, by using a cellular detection system based on a metal response element with green fluorescent protein (MRE-GFP), we demonstrated that Cu could also be released intracellularly from NPs and is accessible for biological processes. Our results indicate that NPs are potential candidates to rebalance metal-ion homeostasis in disease conditions affecting brain and neuronal systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".