X-ray fluorescence measurements of skin iron using an I-125-based system
Bibliographic record
Abstract
Iron overload conditions are a prevalent issue in global healthcare that require the accurate monitoring of iron levels to effectively provide treatment. X-ray fluorescence has emerged as a candidate for a point-of-care measurement tool for the detection of trace elements in vivo. This study explores the feasibility of a portable in vivo x-ray fluorescence (IVXRF) instrument using 125I as a point-of-care device in measuring skin iron levels. The system was calibrated using iron-doped water phantoms for various physiologically-applicable iron concentrations. Measurements were conducted on ex vivo rat skin samples (n = 34), which were then compared to a benchmark laboratory-based XRF system. Monte Carlo modelling using MCNP 6.2 was used to simulate the system in different conditions and provide an estimate of the radiation dose of the system on soft tissue. The R2 value for the calibration line of iron concentration in ppm to normalized iron signal was determined to be 0.985 (p < 0.01). For a measurement period of 1800 s real-time, the minimum detectable limit (MDL) of the system is 3.86 ± 0.06 ppm of iron. The R2 value for the linear regression between the IVXRF and benchmark XRF system normalized iron signals was 0.731 (p < 0.01). The R2 value for the linear regression between the IVXRF normalized iron signal and sample injected iron dose was 0.719 (p < 0.01), meaning the system can distinguish between different iron levels in rat skin. From the Monte Carlo simulations, the expected effective dose contribution from the IVXRF system is 101.68 ± 0.03 nSv. The IVXRF system was shown to accurately measure iron concentrations in ex vivo rat skin samples within the iron concentration ranges found within healthy and iron-overloaded patients. Further work shall be conducted to validate the system in in vivo applications.
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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".