Quantifying Cytotoxicity and Cellular Uptake of Naked Gold Nanoparticles Using Total Reflection X‐Ray Fluorescence
Bibliographic record
Abstract
ABSTRACT The study of the cytotoxic effects of gold nanoparticles (AuNP) is an active area of research. However, there is a lack of consistency in results with measures of cellular uptake relying on inductively coupled plasma (ICP)‐based methods, which introduce inconsistencies with sample preparation. There are also few papers examining the influence that the absolute amount of gold taken up by the cell, rather than the gold nanoparticle itself, has on toxicity, relying on ICP methods. In this work, total reflection x‐ray fluorescence (TXRF) spectroscopy was used to measure AuNP and the absolute amount of gold in epithelial breast cancer cells, and the cytotoxicity of 10 and 50 nm gold nano spheres and rods was analyzed with flow cytometry. With the calculation of absolute gold mass taken up by cancer cells from measurements of gold concentration, 10 nm diameter AuNP were found to be up to 6% more toxic than 50 nm. Our results agree with previous findings that 50 nm spheres are internalized most effectively, and rods are more toxic than spheres for the same number of nanoparticles. Both the linear quadratic and Hill model are suitable models to estimate the cytotoxicity of gold from AuNP uptake, exhibiting a better fit than the exponential model ( p < 0.05). This work shows that TXRF has excellent quantitative abilities for cellular and biological samples.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".