Selectins Ligand Decorated Drug Carriers for Activated Endothelial Cell Targeting
Bibliographic record
Abstract
New active particulate polymeric vectors based on branched polyester copolymers of hydroxy-acid and allyl glycidyl ether were developed to target drugs to the inflammatory endothelial cell surface. The hydroxyl and carboxyl derivatives of these polymers allow grafting of ligand molecules on the polyester backbones at different densities. A known potent nonselective selectin ligand was selected and synthesized using a new scheme. This synthesis allowed the grafting of the ligand to the polyester polymers, preserving its binding activity as assessed by docking simulations. Selectin expression on human umbilical cord vascular endothelial cells (HUVEC) was induced with the pro-inflammatory bacterial lipopolysaccharide (LPS) or with the nonselective inhibitor of nitric oxide synthase L-NAME. Strong adhesion of the ligand decorated nanoparticles was evidenced in vitro on activated HUVEC. Binding of nanoparticles bearing ligand molecules could be efficiently inhibited by prior incubation of cells with free ligand, demonstrating that adhesion of the nanoparticles is mediated by specific interaction between the ligand and the selectin receptors. These nanoparticles could be used for specific drug delivery to the activated vascular endothelium, suggesting their application in the treatment of diseases with an inflammatory component such as rheumatoid arthritis and cancer.
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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".