Homing Peptide-Conjugated Gold Nanorods: The Effect of Amino Acid Sequence Display on Nanorod Uptake and Cellular Proliferation
Bibliographic record
Abstract
Gold nanorods (GNRs) have attracted significant interest in the field of medicine as theranostic agents for both imaging and photothermal ablation of cancerous cells/tissues. Targeting theranostic GNRs specifically to cancer cells is necessary to enhance treatment efficacy and minimize undesired side effects. In this study, targeting functionalized GNR to EphA2 receptors that are overexpressed on prostate cancer cells was investigated as a strategy to achieve enhanced GNR uptake by cancer cells. In addition, the influence of targeting peptide orientation on functionalized GNR uptake by PC-3 cells was explored. GNRs of aspect ratio 4 were functionalized with an EphA2 homing peptide, YSA, using a layer-by-layer polypeptide wrapping approach. In parallel, an analogous population of YSA-modified GNRs, which display a reversed YSA peptide, with the N- and C- termini reversed, was also prepared. GC-MS analysis of the YSA-GNRs indicated that functionalized GNRs displayed approximately 3000 peptides/GNR. The functionalized GNRs remained well-dispersed in biological media for short times (<24 h). An increase in GNRs uptake of the YSA-GNRs by PC-3 cells, compared to the reversed YSA-GNRs, was observed under identical incubation conditions. Lastly, the effect of the YSA-GNRs binding to EphA2 receptors on prostate cancer cell proliferation was also studied. The YSA-functionalized GNRs inhibit PC-3 proliferation at a significantly lower effective dose than free YSA. Overall, the polypeptide LBL deposition technique provides a facile route to target nanoparticles to overexpressed cellular receptors, with the caveat that the specific orientation and display of the targeting moiety plays a critical role in the interaction between the nanoparticle and the cell.
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".