Re-examining the Size/Charge Paradigm: Differing in Vivo Characteristics of Size- and Charge-Matched Mesoporous Silica Nanoparticles
Bibliographic record
Abstract
The combination of nanoparticle (NP) size, charge, and surface chemistry (e.g., extent of modification with polyethylene glycol (PEG)) is accepted as a key determinant of NP/cellular interactions. However, the influence of spatial arrangement and accessibility of the charged molecules on the NP surface vis-à-vis the average surface charge (zeta (ζ) potential) is incompletely understood. Here we demonstrate that two types of mesoporous silica nanoparticles (MSNP) that are matched in terms of primary and hydrodynamic particle size, shape, pore structure, colloidal stability, and ζ potential, but differ in surface chemistry, viz. the spatial arrangement and relative exposure of surface amines, have profoundly different interactions with cells and tissues when evaluated in vitro and in vivo. While both particles are ∼50 nm in diameter, PEGylated, and positively charged (ζ = +40 mV), PEG-PEI (MSNPs modified with exposed polyamines), but not PEG-NMe3(+) (MSNP modified with distributed, obstructed amines) rapidly bind serum proteins, diverse cells types in vitro, and endothelial and white blood cells in vivo (ex ovo chick embryo model). This finding helps elucidate the relative role of surface exposure of charged molecules vs ζ potential in otherwise physicochemically matched MSNP and highlights protein corona neutrality as an important design consideration when synthesizing cationic NPs for biological applications.
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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 | 0.001 |
| 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".