Short-Chain Alkanethiol Coating for Small-Size Gold Nanoparticles Supporting Protein Stability
Bibliographic record
Abstract
The application of gold nanoparticles (AuNPs) is emerging in many fields, raising the need for a systematic investigation on their safety. In particular, for biomedical purposes, a relevant issue are certainly AuNP interactions with biomolecules, among which proteins are the most abundant ones. Elucidating the effects of those interactions on protein structure and on nanoparticle stability is a major task towards understanding their mechanisms at a molecular level. We investigated the interaction of the 3-mercaptopropionic acid coating of AuNPs (MPA-AuNPs) with β2-microglobulin (β2m), which is a paradigmatic amyloidogenic protein. To this aim, we prepared and characterized MPA-AuNPs with an average diameter of 3.6 nm and we employed NMR spectroscopy and fluorescence spectroscopy to probe protein structure perturbations. We found that β2m interacts with MPA-AuNPs through a highly localized patch maintaining its overall native structure with minor conformational changes. The interaction causes the reversible precipitation of clusters that can be easily re-dispersed through brief sonication.
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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) | 0.001 | 0.001 |
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".