Biomonitoring of exposure to metals and metalloids using toenail and fingernail sampling in individuals from artisanal gold mining areas in Mali
Bibliographic record
Abstract
Artisanal gold mining can release metallic elements in the environment that can result in occupational and environmental exposures. This pilot study aimed to assess exposure to metals and metalloids from fingernail and toenail samples in artisanal gold mine workers, inhabitants of a mining and non-mining village in Mali. As it can be particularly challenging to collect and transport biological samples from remote areas, nail sample collection was tested as a potential choice for multielement biomonitoring. A convenience sampling of 315 individuals was performed equally distributed in each location group (105 per location) and stratified by populational group (male adults, female adults, and people <18 years). Toenail and fingernail samples were collected from each participant and twenty-one elements (aluminum (Al), arsenic (As), barium (Ba), beryllium (Be), cadmium (Cd), chromium (Cr), cobalt (Co), copper (Cu), gallium (Ga), iron (Fe), lithium (Li), lead (Pb), manganese (Mn), nickel (Ni), selenium (Se), silver (Ag), strontium (Sr), thallium (Th), uranium (u), vanadium (v) and zinc (Zn)) were quantified. Concentrations of 12 elements in fingernails and/or toenails were significantly higher in the mine worker group, in particular As, Co and Cu in both toenails and fingernails. In the mine worker group specifically, As concentrations in both fingernails and toenails were higher in males. Most metals also had a strong positive correlation overall. Both fingernails and toenails appear as interesting biomonitoring matrices for multielement exposure assessment with an impact of different variables, such as mining exposure and sex, on internal levels. The study also highlighted the importance of further human exposure assessment related to artisanal gold mining in Mali, including the identification of other environmental sources of exposure.
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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 | 0.001 | 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".