A simple method based on ICP-MS for estimation of background levels of arsenic, cadmium, copper, manganese, nickel, lead, and selenium in blood of the Brazillian population
Bibliographic record
Abstract
Throughout the world, biomonitoring has become the standard for assessing exposure of individuals to toxic elements as well as for responding to serious environmental public health problems. However, extensive biomonitoring surveys require rapid and simple analytical methods. Thus, a simple and high-throughput method is proposed for the determination of arsenic (As), cadmium (Cd), copper (Cu), manganese (Mn), nickel (Ni), lead (Pb), and selenium (Se) in blood samples by using inductively coupled plasma–mass spectrometry (ICPMS). Prior to analysis, 200 ml of blood samples was mixed with 500 ml of 10% v/v tetramethylammonium hydroxide (TMAH) solution, incubated for 10 min, and subsequently diluted to 10 ml with a solution containing 0.05% w/v ethylenediamine tetraacetic acid (EDTA) + 0.005% v/v Triton X-100. After that, samples were directly analyzed by ICP-MS (ELAN DRC II). Rhodium was selected as an internal standard with matrix-matching calibration. Method detection limits were 0.08, 0.04, 0.5, 0.09, 0.12, 0.04, and 0.1 mg//L for As, Cd, Cu, Mn, Ni, Pb, and Se, respectively. Validation data are provided based on the analysis of blood samples from the trace elements inter-\\comparison program operated by the Institut National de Santé Publique du Quebec, Canada. Additional validation was provided by the analysis of human blood samples by the proposed method and by using electrothermal atomic absorption spectrometry (ETAAS). The method was subsequently applied for the estimation of background metal blood values in the Brazilian population. In general, the mean concentrations of As, Cd, Cu, Mn, Ni, Pb, and Se in blood were 1.1, 0.4, 890, 9.6, 2.1, 65.4, and 89.3 mg/L, respectively, and are in agreement with other global populations. Influences of age, gender, smoking habits, alcohol consumption, and geographical variation on the values were also considered. Smoking habits influenced the levels of Cd in blood. The levels of Cu, Mn, and Pb were significantly correlated with gender, whereas Cu and Pb were significantly correlated with age. There were also interesting differences in Mn and Se levels in the population living in the north of Brazil compared to the south.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".