Surface Adsorption and Molecular Modeling of Biofunctional Gold Nanoparticles for Systemic Circulation and Biological Sustainability
Bibliographic record
Abstract
Fundamental aspects of protein complex coated gold nanoparticles (Au NPs) were presented for their possible use in systemic circulation in terms of pharmaceutical formulations. For this purpose, protein complexes of bovine serum albumin (BSA), lysozyme (Lys), and zein with an industrial important bioactive polymer diethylaminoethyl dextran (DEAE) were studied in the presence of Au NPs. Surface adsorption of such complexes magnified DEAE–protein interactions which were easily monitored spectroscopically under the effect of temperature and reaction time. In vitro synthesis of Au NPs allowed a simultaneous adsorption of the DEAE–protein complex on the NP surface to achieve colloidal stability, which was dramatically influenced by the nature of the DEAE–protein complex. The DEAE–BSA complex demonstrated strong favorable mainly electrostatic interactions followed by DEAE–Lys, while DEAE–zein interactions were predominantly influenced by the hydrophobic nature of zein. At the molecular level, the interactions were evaluated from the molecular dynamics (MD) studies which focused on the protein surface charge, dihedral angle variations, and protein unfolding upon dextran–protein complexation as well as its surface adsorption. MD studies further helped us to identify specific amino acid residues which promoted such interactions among the DEAE and protein as well as the surface adsorption of DEAE–protein complexes and allowed the synthesis of suitable biofunctional Au NPs with interesting bioapplicability with blood cells. Biofunctional NPs coated with DEAE–BSA and DEAE–Lys complexes over the entire mixing range proved to be the best suited vehicles for biomedical applications in systemic circulation.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".