Investigating the impact of internal standard heterogeneity on gold quantification with total reflection X‐ray fluorescence: A simulation study
Bibliographic record
Abstract
Total reflection X‐ray Fluorescence (TXRF) is a powerful analytical tool with high detection sensitivity that has been applied to a variety of biological samples. While its ability to quantify gold nanoparticles (AuNPs) in cancer cells has been demonstrated, the extension to tissue slices would be of interest. To that end, the preservation of the underlying tissue microstructure requires samples to be measured as microtome slices. In this form, internal standard spiking is warranted. Thus, it is important to examine the impact of sample heterogeneity on the TXRF's quantification accuracy. To address these questions, a TXRF spectrometer along with 5 μm thin heterogeneous and homogeneous samples were modeled using TOPAS. The simulation model generated TXRF spectra which were then analyzed to obtain recovery rates of Au in both sample types. The results showed near 100% recovery regardless of the elemental spatial distribution in the samples. This provides insights into the quantification potential for AuNPs inside tumors that are histologically processed into thin tissue slices. In addition, this simulation toolkit provides the first practical means of modeling TXRF spectroscopy which will hopefully be of use to the TXRF community.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".