Feasibility of measuring arsenic and selenium in human skin using <i>in vivo</i> x-ray fluorescence (XRF)—a comparison of methods
Bibliographic record
Abstract
In recent years, in vivo measurement systems of arsenic in skin by K-shell x-ray fluorescence (XRF) have been developed, including one which was applied in a pilot study of human subjects. Improved tube-based approaches suggest the method can be further exploited for in vivo studies. Recently, it has been suggested that selenium deficiency is correlated with arsenic toxicity. A non-invasive measurement of both elements could therefore be of potential interest. The main aim of this current study was to evaluate and compare the performance of an upgraded portable XRF system and an advanced version of the benchtop XRF system for both selenium and arsenic. This evaluation was performed in terms of arsenic and selenium Kα detection limits for a 4W gold anode Olympus InnovX Delta portable analyzer (40 kVp) in polyester resin skin-mimicking phantoms. Unlike the polychromatic source earlier reported in the literature, the benchtop tube-based technique involves monochromatic excitation (25 W silver anode, manufactured by x-ray optics, XOS) and a higher throughput detector type. Use of a single exciting energy allows for a lower in vivo dose delivered and superior signal-noise ratio. For the portable XRF method, arsenic and selenium minimum detection limits (MDLs) of 0.59 ± 0.03 ppm and 0.75 ± 0.02 ppm respectively were found for 1 min measurement times. The MDLs for arsenic and selenium using the benchtop system were found to be 0.35 ± 0.01 ppm and 0.670 ± 0.004 ppm respectively for 30 min measurement times. In terms of a figure of merit (FOM), allowing for dose as well as MDL, the benchtop system was found to be superior for arsenic and the two systems were equivalent, within error, for selenium. We shall discuss the performance and possible improvements of each system, their ease of use and potential for field application.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".