Quantification of gold nanoparticles in histologically thin tissue slices using <scp>TXRF</scp>
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The promise of gold nanoparticles (AuNPs) in cancer applications remains an active area of research. The assessment of tumoral uptake can provide valuable insights into their intended efficacy. Total X‐ray reflection fluorescence (TXRF) spectroscopy offers low detection limits coupled with direct quantification through internal standardization. These features enable TXRF to measure uptake of AuNPs in the presence of organic matrix. In this work, we demonstrate TXRF's ability to directly quantify AuNP concentration in slices of tissue. Bovine liver was cut into 5 μm thin slices, and 10 nm reference material AuNPs were deposited either above or below the tissue. The tissue slice was then spiked with a lanthanum (La) internal standard. In order to extend the investigation to homogenous samples, a TOPAS‐based simulation toolkit was used to model Au‐containing tissue. Additionally, scanning electron microscopy (SEM) was used to examine the distribution of the Au and La on the tissue slices, revealing elemental uniformity on the tissue surface. The experimental and simulation results revealed nearly 100% quantification accuracy of AuNPs in all permutations of sample configuration—making TXRF a viable option for assessment of tumoral AuNP uptake with minimal sample preparation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it