Temporal variability of nickel levels using repeated biomonitoring data
Bibliographic record
Abstract
BACKGROUND AND AIM: The toxicity of nickel to humans is well known, but studies on exposure levels related to metabolism in the human body are insufficient. We studied the distribution of nickel in three biological tissues in the body. METHOD: We recruited 50 healthy participants living in Busan, South Korea. Biological tissues were collected four times in March 2020, August 2020, June 2021, and November 2021. Nickel concentrations were analyzed using inductively coupled plasma-mass spectrometry (ICP-MS) at Dong-A University Environmental Health Center. RESULTS: The results showed that the accuracy and precision of all three tissues were within ±15%, and the geometric mean (GM) of nickel concentration in blood was high in November (blood: 1.197 µg/L), and the GM of nickel concentration in serum and urine was high in March (serum: 1.146 µg/L, urine: 1. 893 µg/L). Temporal variation had a significantly affected nickel levels in blood (p-value: 0.004), serum (p-value: 0.001), and urine (p-value: 0.001). The intraclass correlations of blood and urinary nickel were 8.05% and 6.4%, respectively. The GM of blood nickel in Italy (0.89 µg/L) was comparable to this study (1.03 µg/L), but serum nickel in Italy (0.35 µg/L) was about a-half of this study (0.69 µg/L). Urinary nickel was compared to international reference values (RV95), and no participants exceeded the RV95 values for Canada, France, or Germany. The correlation coefficient of nickel concentration in the three tissues was low. This study shows that nickel levels in the body vary depending on the time of sampling and that the intra-individual correlation is greater than the inter-individual correlation. CONCLUSIONS: Therefore, future research is needed on biomarkers that can adequately reflect the level of nickel in the body by considering the source and route of exposure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 | 0.001 |
| 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 teacher head, 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".