Liquid chromatography-based metallomics and transmission electron microscopy reveal gold nanoparticle surface treatment with vicinal dithiols to abolish protein corona formation
Bibliographic record
Abstract
AIMS: While gold nanoparticles (AuNPs) should allow the delivery of surface immobilized drugs to intended target tissues via the bloodstream, their interactions with plasma proteins may induce their aggregation and thus impede an effective delivery of chemotherapeutic agents to target tissues. The deliberate surface treatment of AuNPs has the potential to overcome this inherent limitation. METHODS: To probe interactions between surface treated AuNPs in blood plasma, we employed a size-exclusion chromatography (SEC)-based metallomics tool together with transmission electron microscopy (TEM). RESULTS: After the addition of citrate capped AuNPs to plasma, its metallomics analysis revealed a >670 kDa Au species, which TEM analysis identified as AuNP-plasma protein aggregates. To ameliorate the formation of the latter, the surface of citrate capped AuNPs was modified with dithiothreitol (DTT), meso 2,3-dimercaptosuccinic acid (DMSA), or 2,3 dimercapto-1-propionesulfonic acid (DMPS) and the effect of this surface treatment was probed after the addition of these modified AuNPs to rabbit plasma. The results for DMSA/DMPS-treated AuNPs revealed that the tight binding of these dithiols more significantly reduced protein corona formation compared to DTT-AuNPs implying that the surface treatment of AuNPs with DMSA or DMPS is a feasible strategy to control protein corona formation and thus their aggregation in plasma. CONCLUSIONS: The AuNP-based delivery of immobilized drugs using targeting sequences to cancer tissues can be enhanced by their surface treatment with DMSA or DMPS. Since dithiols left over after the AuNP surface treatment mobilized iron from plasma metalloproteins, excess dithiols must be removed before injecting patients.
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.
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) | 0.002 | 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".