Quantification of gold nanoparticles in histologically thin tissue slices using <scp>TXRF</scp>
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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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".