Integration of Peptides for Enhanced Uptake of PEGylayed Gold Nanoparticles
Bibliographic record
Abstract
Polyethylene glycol (PEG) has promoted the prospective applications of nanoparticles (NPs) in cancer therapy. PEG is used to evade the immune system allowing NPs accumulation within the tumor using its leaky vasculature. However, the cellular uptake of PEG-coated (PEGylated) NPs is lower in comparison to non-PEGylated NPs since PEG minimizes surface binding of ligands that mediate NP endocytosis. For improved outcome in therapeutic applications, it is necessary to enhance the uptake of PEGylated NPs. We added a peptide containing an integrin binding domain known as the RGD sequence to the NP surface in addition to PEG. We used gold NPs (GNPs) of sizes 14, 50, and 70 nm in this study. Our in vitro data for HeLa cells show enhanced uptake for NPs coated with both PEG and the peptide in comparison to PEGylated GNPs. NPs of size 50 nm had the highest uptake among the three sizes for all GNP surfaces. A similar size-dependent trend was observed for MDA-MB-231 cells for as-made GNPs with lower uptake in comparison to HeLa cells. However, only 14 nm peptide-modified PEGylated NPs had enhanced uptake. Hence, NP uptake was found dependent on cell type and NP surface properties. A properly designed NP system with both PEG and cell membrane targeting peptides can be used to protect it from the immune system and promote internalization by cells upon entry into tumor environment.
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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) | 0.000 | 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".