Multiparametric cytotoxicity profiling reveals cell-line and ligand-dependent toxicity for pegylated gold nanoparticles (AuNP-PEG)
Bibliographic record
Abstract
The highly tunable surface chemistry of gold nanoparticles (AuNPs) makes them ideal candidates for cancer treatments. Modification of AuNP surface chemistry creates linkage points for different surface coatings whose chemical structure regulates AuNP interactions with cells and thus plays a key role in AuNP cytotoxicity. This study looked at AuNPs functionalized with three polyethylene glycol (PEG) coatings, differing in end group functionality: PEG methyl terminated thiol (PEGCH3), PEG amine terminated thiol (PEGNH2), and PEG carboxylic acid terminated thiol (PEGCOOH). Cytotoxic effects were compared across three cell lines: human embryonic kidney (HEK293T/17), prostate cancer (PC-3), and ovarian cancer (SKOV3). Biochemical assays measured the effect AuNPs elicit on the ability of single cells to form colonies, metabolize thiazolyl blue tetrazolium bromide (MTT), or produce reactive oxygen species (ROS) using 2′,7′-dichlorofluorescein. Overall, AuNP-PEG particles were minimally toxic. HEK293T/17 colony formation was significantly decreased with all but PEGCOOH particle types, and PEGNH2 treatments significantly decreased colony formation for all three tested cell lines. ROS production was significantly increased when treated with 100 µg mL−1 AuNP PEGNH2 in all three cell lines, with PEGCH3 also showing increased ROS in PC-3 cells. PEGCH3 reduced metabolic function (MTT metabolism) in only SKOV3 cells, while PEGCOOH was toxic to HEK293T/17 cells at 100 µg mL−1. These results suggest that differing end group chemistry leads to modest cytotoxic profiles for each AuNP that are cell line and coating dependent. Elucidation of AuNP mechanisms of toxicity is a critical step in the evaluation of the future therapeutic potential for these particles.
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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) | 0.000 | 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".