Elucidating the Influences of Size, Surface Chemistry, and Dynamic Flow on Cellular Association of Nanoparticles Made by Polymerization‐Induced Self‐Assembly
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it