Surface Functionalization of Nanomaterials with Dendritic Groups: Toward Enhanced Binding to Biological Targets
Bibliographic record
Abstract
A diverse array of nanomaterials ranging from polymer assemblies to nanoparticles has been under development for biomedical applications in recent years. A key aspect of these applications is the ability to target the materials to the desired locations in vivo by exploiting their size or through the conjugation of active targeting groups. While nanoscale scaffolds may provide advantages such as the multivalent presentation of targeting ligands, the binding of these ligands may also be inhibited by interfering polymer chains at their surfaces. This aspect was investigated here by preparing poly(butadiene-block-ethylene oxide) vesicles and dextran-coated iron oxide nanoparticles functionalized with dendritic and nondendritic displays of mannose, a well-known multivalent ligand. The binding of these systems to the mannose-binding protein Concanavalin A was compared using a hemagglutination assay. It was found that the dendritic systems exhibited 1-2 orders of magnitude enhancement in binding affinity relative to the nondendritic displays. This result is attributed to the ability of the dendritic groups to overcome steric inhibition by polymer chains at the material surface and also to the presentation of ligands in localized clusters. It is anticipated that these results should be applicable to a wide range of nanomaterials with polymers at their surfaces and that the method by which biological ligands are conjugated to the surfaces of nanoparticles and polymer assemblies should be carefully considered.
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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".