Peptide modified gold nanoparticles for improved cellular uptake, nuclear transport, and intracellular retention
Bibliographic record
Abstract
Gold nanoparticles (GNPs) are being extensively used in cancer therapeutic applications due to their ability to act both as an anticancer drug carrier in chemotherapy and as a dose enhancer in radiotherapy. The therapeutic response can be further enhanced if nanoparticles (NPs) can be effectively targeted into the nucleus. Here, we present an uptake and removal of GNPs functionalized with three peptides. The first peptide (RGD peptide) enhanced the uptake, the second peptide (NLS peptide) facilitated the nuclear delivery, while the third one (pentapeptide) covered the rest of the surface and protected it from the binding of serum proteins onto the NP surface. The pentapeptide also stabilized the conjugated GNP complex. The peptide-capped GNPs showed a five-fold increase in NP uptake followed by effective nuclear localization. The fraction of NPs exocytosed was less for peptide-capped NPs as compared to citrate-capped ones. Enhanced uptake and prolonged intracellular retention of peptide-capped GNPs could allow NPs to perform their desired applications more efficiently in cells. These studies will provide guidelines for developing NPs for therapeutic applications, which will require "controlling" of the NP accumulation rate while maintaining low toxicity.
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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".