In Situ Characterization of the Protein Corona of Nanoparticles In Vitro and In Vivo
Bibliographic record
Abstract
A new theoretical framework that enables the use of differential dynamic microscopy (DDM) in fluorescence imaging mode to quantify in situ protein adsorption onto nanoparticles (NP) while simultaneously monitoring for NP aggregation is proposed. This methodology is used to elucidate the thermodynamic and kinetic properties of the protein corona (PC) in vitro and in vivo. The results show that protein adsorption triggers particle aggregation over a wide concentration range and that the formed aggregate structures can be quantified using the proposed methodology. Protein affinity for polystyrene (PS) NPs is observed to be dependent on particle concentration. For complex protein mixtures, this methodology identifies that the PC composition changes with the dilution of serum proteins, demonstrating a Vroman effect never quantitatively assessed in situ on NPs. Finally, DDM allows monitoring of the evolution of the PC in vivo. This results show that the PC composition evolves significantly over time in zebrafish larvae, confirming the inherently dynamic nature of the PC. The performance of the developed methodology allows to obtain quantitative insights into nano-bio interactions in a vast array of physiologically relevant conditions that will serve to further improve the design of nanomedicine.
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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".