Elucidating the Influences of Size, Surface Chemistry, and Dynamic Flow on Cellular Association of Nanoparticles Made by Polymerization‐Induced Self‐Assembly
Bibliographic record
Abstract
The size and surface chemistry of nanoparticles dictate their interactions with biological systems. However, it remains unclear how these key physicochemical properties affect the cellular association of nanoparticles under dynamic flow conditions encountered in human vascular networks. Here, the facile synthesis of novel fluorescent nanoparticles with tunable sizes and surface chemistries and their association with primary human umbilical vein endothelial cells (HUVECs) is reported. First, a one-pot polymerization-induced self-assembly (PISA) methodology is developed to covalently incorporate a commercially available fluorescent dye into the nanoparticle core and tune nanoparticle size and surface chemistry. To characterize cellular association under flow, HUVECs are cultured onto the surface of a synthetic microvascular network embedded in a microfluidic device (SynVivo, INC). Interestingly, increasing the size of carboxylic acid-functionalized nanoparticles leads to higher cellular association under static conditions but lower cellular association under flow conditions, whereas increasing the size of tertiary amine-decorated nanoparticles results in a higher level of cellular association, under both static and flow conditions. These findings provide new insights into the interactions between polymeric nanomaterials and endothelial cells. Altogether, this work establishes innovative methods for the facile synthesis and biological characterization of polymeric nanomaterials for various potential 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 | 0.000 |
| 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".