Ultrasmall gold nanorods: synthesis and glycocalyx-related permeability in human endothelial cells
Bibliographic record
Abstract
BACKGROUND: Clinical data show shed endothelial glycocalyx (GCX) components in blood samples of atherosclerotic patients, linking atherosclerotic development to endothelial GCX integrity. Healthy GCX has pores no >7 nm, and shed GCX has even larger pores. Therefore, we suggest targeting and treating atherosclerosis-prone blood vessels by using nanoscale vehicles to deliver drugs via the nanoscale GCX as it becomes dysfunctional. MATERIALS AND METHODS: To test our idea, we investigated permeability of nanoparticles in endothelium, as related to a GCX expression. The present work involves nanorods, which are expected to interact with larger portions of endothelial cell (EC) membranes, due to surface area of the nanorod long axis. Conventional nanorod diameters are orders of magnitude larger than the GCX pore size, so we adapted conventional synthesis methods to fabricate ultrasmall gold nanorods (GNRs). Our ultrasmall GNRs have an aspect ratio of 3.4, with a length of 27.9±3.1 nm and a diameter of 8.2±1.4 nm. In addition, we produced GNRs that are biocompatible and fluorescently visible, by coating the surface with functionalized polyethylene glycol and Alexa Fluor 647. To study GNR-GCX interactions, we used human ECs, for species relevance. RESULTS: Under life-like flow conditions, the human ECs are densely covered with a 1.3 µm thick layer of GCX, which coincides with minimal GNR permeability. When the GCX is weakened from lack of flow (static culture) or the presence of GCX degradation enzyme in the flow stream, the GCX shows 40% and 60% decreased thickness, respectively. GCX weakness due to lack of flow only slightly increases cellular permeability to GNRs, while GCX weakness due to the presence of enzyme in the flow leads to substantial increase in GNR permeability. CONCLUSION: These results clarify that the GCX structure is an avenue through which drug-carrying nanoparticles can be delivered for targeting affected blood vessels to treat atherosclerosis.
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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".