SELENIUM AND MERCURY OPPOSING INFLUENCES IN FISH-EATING POPULATIONS OF THE BRAZILIAN AMAZON
Bibliographic record
Abstract
Background and Aims: Amazonian riverside communities have the highest reported mercury (Hg) exposure in the world today. Selenium (Se), an essential element, is involved in several body functions through selenoprotein expression. Some studies suggest that selenium (Se) may be protective for Hg toxicity, however, data from animal and human studies are inconsistent and some epidemiological studies show toxic effects of elevated Se, notably hyperinsulinemia, aloplecia and paraesthesia. The objective of the present study was to examine the relations between biomarkers of Se and visual and motor functions, taking into account co-variables and Hg exposure. Methods: Participants (n = 448, 15-87y), were recruited from 12 communities along the Tapajós River. B-Se, P-Se and blood Hg (B-Hg) were measured by ICP-MS. Interview-administered questionnaires served to collect information on sociodemographics and medical history. All participants underwent a complete visual examination and performed several tests of motor functions. Results: Se status ranged from normal to high (B-Se median: 228μg/L, range 103-1500μg/L). Few participants reported diabetes (1.1%), and despite high levels of Se in some individuals, no signs and symptoms of Se toxicity were observed. P-Se concentrations were associated with beneficial outcomes: lower prevalence of age-related cataracts, better near visual acuity and motor performance; regression estimates were stronger when adjusting for B-Hg. When stratifying at the median B-Hg concentrations, P-Se consistently presented associations with the outcomes only at high B-Hg concentrations. Conclusions: In this population with high Hg exposure, Se intake may play a role in offsetting some deleterious effects of Hg, but the beneficial effects of Se may not be present in populations with low Hg exposure, and Se may not offset all Hginduced toxic effects in fish-eating populations. Further studies should address the risks and benefits of dietary Se in order to better understand the complex Se-Hg interactions in human populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".