Toxic risks and nutritional benefits of traditional diet on near visual contrast sensitivity and color vision in the Brazilian Amazon
Bibliographic record
Abstract
BACKGROUND: Visual functions are known to be sensitive to toxins such as mercury (Hg) and lead (Pb), while omega-3 fatty acids (FA) and selenium (Se) may be protective. In the Tapajós region of the Brazilian Amazon, all of these elements are present in the local diet. OBJECTIVE: Examine how near visual contrast sensitivity and acquired color vision loss vary with biomarkers of toxic exposures (Hg and Pb) and the nutrients Se and omega-3 FA in riverside communities of the Tapajós. METHODS: Complete visuo-ocular examinations were performed. Near visual contrast sensitivity and color vision were assessed in 228 participants (≥15 years) without diagnosed age-related cataracts or ocular pathologies and with near visual acuity refracted to at least 20/40. Biomarkers of Hg (hair), Pb (blood), Se (plasma), and the omega-3 FAs eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) in plasma phospholipids were measured. Multiple linear regressions were used to examine the relations between visual outcomes and biomarkers, taking into account age, sex, drinking and smoking. RESULTS: Reduced contrast sensitivity at all spatial frequencies was associated with hair Hg, while %EPA, and to a lesser extent %EPA+DHA, were associated with better visual function. The intermediate spatial frequency of contrast sensitivity (12 cycles/degree) was negatively related to blood Pb and positively associated with plasma Se. Acquired color vision loss increased with hair Hg and decreased with plasma Se and %EPA. CONCLUSIONS: These findings suggest that the local diet of riverside communities of the Amazon contain toxic substances that can have deleterious effects on vision as well as nutrients that are beneficial for visual function. Since remediation at the source is a long process, a better knowledge of the nutrient content and health effects of traditional foods would be useful to minimize harmful effects of Hg and Pb exposure.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".