The measurement of trace elements in human nails and nail clippings using portable <scp>X‐ray</scp> fluorescence: A review
Bibliographic record
Abstract
A variety of biomarkers are available for the assessment of human exposure to, and absorption of, trace elements. The measurement of trace elements in nail or nail clippings offers some benefits relative to other body sites. Typically, concentrations of elements are assessed in clippings, using high precision methods such as inductively coupled plasma mass spectrometry or instrumental neutron activation analysis. Over the last decade, portable X‐ray fluorescence (pXRF) has emerged as a novel method with potential advantages for the measurement of trace elements in nail and nail clippings. This review paper examines early developments and recent work in the use of pXRF to measure elements such as As, Se, Mn, Zn, Cr, Pb, and Hg in nails and nail clippings. While initial pXRF studies were limited to the use of nail phantoms and nail clipping phantoms, more recent work has applied the pXRF method with real nail clippings and nails. Results have been promising, but a number of issues have emerged requiring additional attention. These relate to the analysis of X‐ray spectra, selection of X‐ray beam for excitation, method validation and normalization, and practical aspects of study design. With consideration of these issues, pXRF offers significant potential to provide a straightforward, economical, and rapid measurement approach to help improve our understanding of trace element exposure and related health outcomes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".