Reference concentrations for trace elements in urine for the Brazilian population based on q-ICP-MS with a simple dilute-and-shoot procedure
Bibliographic record
Abstract
Biomonitoring of trace elements is of critical importance in human health assessment. However, trace element concentrations in biological fluids are affected by environmental and physiological parameters, and therefore considerable variations can occur between specific population subgroups. Brazil is a large country with large environmental diversity and with a limited knowledge of the reference values (baseline data) for trace elements in biological fluids. Atomic absorption spectrometry (AAS) and inductively coupled plasma emission spectrometry (ICP-OES) are still the dominant analytical techniques used for biomonitoring trace element analysis in clinical specimens. However, the use of ICP-MS is becoming more usual in clinical laboratory analysis. Then, we evaluated here a simple dilute-and-shoot method for sequential determination of Al, Ba, Be, Cd, Co, Cu, Cs, Mn, Ni, Pb, Pt, Sb, Se, Sn, Tl and U in urine by quadrupole inductively coupled plasma mass spectrometry (q-ICP-MS). Urine samples (500 µL) were accurately pipetted into conical tubes (15 mL) and diluted to 10 mL with a solution containing 0.5 % (v/v) HNO3 + 0.005% (v/v) Triton X-100. Diluted urine samples also contain rhodium, iridium and yttrium added as internal standards. After that, samples were directly analyzed by ICP-MS against matrix-matching calibration. Method detection limit (3s, n = 20) were in the ng L-1 range for all analytes. The method was applied to the analysis of 412 ordinary urine samples from Brazilian healthy and non-exposed subjects to establish reference values. Data validation was provided by the analysis of the standard reference material (SRM) 2670a toxic elements in urine (freeze-dried) (high and low levels) from the National Institute of Standards and Technology (NIST) and reference urine samples from the trace elements intercomparison program operated by the Institut National de Sante' Publique du Quebec, Canada.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".