The feasibility of K XRF bone lead measurements in mice assessed using 3D-printed phantoms
Bibliographic record
Abstract
Abstract This article describes the development of a system for in vivo measurements of lead body burden in mice using 109 Cd K x-ray fluorescence (XRF). This K XRF system could facilitate early-stage studies on interventions that ameliorate or reverse organ tissue damage from lead poisoning by reducing animal numbers through a cross-sectional study approach. A novel mouse phantom was developed based on a mouse atlas and 3D-printed using PLA plastic with plaster of Paris ’bone’ inserts. PLA plastic was found to be a good surrogate for soft tissue in XRF measurements and the phantoms were found to be good models of mice. As expected, lead detection limits varied with mouse size, mouse orientation, and mouse position with respect to the source and detector. The work suggests that detection limits of 10 to 20 μ g Pb per g bone mineral may be possible for a 2 to 3 hour XRF measurement in a single animal, an adequate limit for some pre-clinical studies. The 109 Cd K XRF mouse measurement system was also modeled using the Monte Carlo code MCNP. The combination of experiment and modeling found that contrary to expectation, accurate measurements of lead levels in mice required calibration using mouse-specific calibration standards due to the coherent scatter peak normalization failing when small animals are measured. MCNP modeling determined that this was because the coherent scatter signal from soft tissue, which until now has been assumed negligible, becomes significant when compared to the coherent scatter signal in bone in small animals. This may have implications for some human measurements. This work suggests that 109 Cd K x-ray fluorescence measurements of lead body burden are precise enough to make the system feasible for small animals if appropriately calibrated. Further work to validate the technology’s measurement accuracy and performance in vivo will be required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".