The effect of phospho-peptide on the stability of gold nanoparticles and drug delivery
Bibliographic record
Abstract
Gold nanoparticles (AuNPs) have been proposed for many applications in medicine and bioanalysis. For use in all these applications, maintaining the stability of AuNPs in solution by suppressing aggregation is paramount. Herein, the effects of amino acids were investigated in stabilizing AuNPs by rationally designed peptide scaffolds. Compared to other tested amino acids, phosphotyrosine (pY) significantly stabilized AuNPs. Our results indicated that pY modified AuNPs presented a high level of stability in various solutions, and had good biocompatibility. When a pY-peptide was used in stabilizing AuNPs, the phosphate group could be removed by phosphatases, which subsequently caused the aggregation and the cargo release of AuNPs. In vitro study showed that AuNPs formed aggregation in a phosphatase concentration depending manner. The aggregation of AuNPs was well correlated with the enzymatic activity (R 2 = 0.994). In many types of cancer, a significant increase in phosphatases has been observed. Herein, we demonstrated that cancer cells treated with pY modified AuNPs in conjunction with doxorubicin killed SGC-7901 cells with high efficiency, indicating that the pY peptide stabilized AuNPs could be used as carriers for targeted drug delivery. In summary, pY peptides can act to stabilize AuNPs in various solutions. In addition, the aggregation of pY-AuNPs could be tuned by phosphatase. These results provide a basis for pY-AuNPs acting as potential drug carriers and anticancer efficacy.
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) | 0.000 | 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".