Sequential Hair Analysis as a Seasonal Biomarker of Variations in Selenium Status
Bibliographic record
Abstract
ISEE-591 Objective: In the Brazilian Amazon, where there is high mercury (Hg) exposure, there is much interest in possible Hg-Se interactions. Se is ingested through a number of food sources, whose content is dependent on soil Se concentrations and plant accumulation. Intraindividual variations over time can occur through changes in dietary habits and seasonal availability of different foods. The present study was conducted among 6 riparian communities of the Tapajós River (Brazil) to evaluate variations in whole blood and sequential hair cm Se biomarkers, and to examine blood-hair and blood-urine relations. Materials and Methods: Two cross-sectional studies were conducted, at the descending water (n=259) and the rising water (n=137) seasons, with repeated measures for a subgroup (n=112). Blood Se (B-Se), hair Se (H-Se), and urine Se (U-Se) were assessed. Sociodemographic information was collected using an interview-administered questionnaire. Results: Blood Se (B-Se) levels presented a large range (142.1 to 2447.8 mg/L) with no overall interseasonal variation (median 284.3 and 292.2 mg/L respectively). Sequential analysis of 13 cm hair strands showed significant variations over time, where Se levels associated to the descending water season were lower compared to the that of rising water season (median 0.7 and 0.9 mg/g, range 0.2–4.3 and 0.2–5.4 mg/g respectively), possibly reflecting seasonal availability of Se sources in local food. No individuals presented Se deficiency, but 10 participants had high Se status, potentially a threat to their health. At both seasons, relations between B-Se and H-Se were linear and highly significative, while the best relation between B-Se and U-Se was described by a sigmoid curve. Gender, age, education, and smoking had no influence on Se status or biomarker relations. Conclusions: While blood represents a single measure in time, sequential analyses of H-Se provides a good reflection of varying Se status and should be useful to study Se-Hg relations.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".