Enhanced uptake of polyethylene glycol coated gold nanoparticles for improved therapeutics
Bibliographic record
Abstract
Polyethylene glycol (PEG) has promoted the prospective cancer treatment applications of gold nanoparticles (GNPs). PEG is widely used in providing GNPs with stealth properties, hence prolonging blood circulation times. GNPs coated with PEG (PEG-GNPs) take advantage of the enhanced permeability and retention effect in tumor environments, making them suitable for targeted treatment. The cellular uptake of PEG-GNPs is significantly lower than uncoated GNPs in vitro. PEG minimizes PEG-GNP interaction with ligands that mediate cancer cell uptake, causing reduced GNP uptake in comparison to uncoated GNP. As intracellular localization of GNPs maximizes its therapeutic enhancement, there is a need to improve the uptake of PEG-GNPs. To improve cell entry, receptor mediated endocytosis peptides were conjugated with PEG-GNPs of varying core sizes. Spherical GNPs of diameters 14, 50 and 70 nm with a PEG chain length of 2 kDa were used to determine a preferred core size for uptake in vitro in HeLa and MDA-MB-231 cells. A preliminary study using surface-modified GNPs as a radiosensitizer to a megavoltage clinical photon beam was done to assess its therapeutic application.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".