Conjugating Ligands to an Equilibrated Nanoparticle Protein Corona Enables Cell Targeting in Serum
Bibliographic record
Abstract
Targeting ligands are conjugated onto nanoparticles to increase their selectivity for diseased cells. However, they become covered by serum proteins which prevent them from binding to target receptors. Here, we show that the nanoparticle protein corona achieved a maximum thickness in serum because the protein adsorption and desorption rates reached an equilibrium. Simulation experiments suggest that the number of molecular interactions between proteins decrease with distance from the nanoparticle surface until the forces are too weak to hold the proteins together. This results in an equilibration state between the proteins on the nanoparticle surface and in biological fluids. Conjugating targeting ligands to this equilibrated protein corona allowed the nanoparticles to bind to target cells in the presence of serum proteins. In contrast, conjugating targeting ligands directly to the nanoparticle surface resulted in a 55% reduction in binding to target cells in serum. We demonstrated this concept using two nanoparticle material types with different surface chemistries. We present a ligand-on-corona conjugation strategy that overcomes the negative impact of serum protein adsorption on nanoparticle cellular targeting.
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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".