Mitigating arsenic toxicity through dietary selenium and biofortified lentils
Bibliographic record
Abstract
Arsenic (As) toxicity causes senous health problems in humans. Selenium (Se), an important micronutrient and antioxidant acts as an antagonist of arsenic (As). Selenium is seriously deficient in Southeast Asian soils, but has good levels in Saskatchewan (SK) soils. We evaluated two kinds of Se manipulated diets in counteracting As toxicity in rats evident through liver damage (peroxidative stress), immunotoxicity (antibody response), depleted glutathione levels, as well as increased As residues in tissue (liver, kidney, whole blood) and excreta (urine, feces). For both studies, higher Se diets (rodent chow and lentil-based feed) resulted in higher glutathione (GSH), lower lipid peroxidative damage, recovered antibody response, higher fecal As excretion and lower renal As residues. These findings support the hypothesis that As toxicity is decreased through naturally high Se lentil diets as well as Se fortified rodent chow by reducing the As induced damage and eliminating As from the body.
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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 | 0.000 |
| 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) | 0.001 | 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".