Assessing the Effect of Surface Chemistry on Gold Nanorod Uptake, Toxicity, and Gene Expression in Mammalian Cells
Bibliographic record
Abstract
Through the use of various layer-by-layer polyelectrolyte (PE) coating schemes, such as the common poly(diallyldimethylammonium chloride)-poly(4-styrenesulfonic acid) (PDADMAC-PSS) system, the mammalian cellular uptake of gold nanorods can be tuned from very high to very low by manipulating the surface charge and functional groups of the PEs. The toxicity of these nanorods is also examined. Since the PE coatings are individually toxic, the toxicity of nanorods coated in these PEs is measured and cells are found to be greater than 90% viable in nearly all cases, even at very high concentrations. This viability assay may not be a complete indicator of toxicity, and thus gene-expression analysis is used to examine the molecular changes of cells exposed to PDADMAC-coated nanorods, which enter cells at the highest concentrations. Indicators of cell stress, such as heat-shock proteins, are not significantly up- or down-regulated following nanorod uptake, which suggests that PDADMAC-coated gold nanorods have negligible impact on cell function. Furthermore, a very low number of genes experience any significant change in expression (0.35% of genes examined). These results indicate that gold nanorods are well suited for therapeutic applications, such as thermal cancer therapy, due to their tunable cell uptake and 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 | 0.000 | 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".