A high-selenium lentil dietary intervention in Bangladesh to counteract arsenic toxicity: study protocol for a randomized controlled trial
Bibliographic record
Abstract
Abstract Background Millions of people worldwide are exposed to dangerous levels of arsenic (above the WHO water standard of 10 ppb) in drinking water and food. Lack of nutritious foods exacerbates the adverse health effects of arsenic poisoning. The micronutrient selenium is a known antagonist to arsenic, promoting the excretion of arsenic from the body. Studies are in progress examining the potential of using selenium supplement pills to counteract arsenic toxicity. We are planning a clinical trial to test whether high-selenium lentils, as a whole food solution, can improve the health of arsenic-exposed Bangladeshi villagers. Methods/design A total of 400 participants (about 80 families) will be divided into two groups via computer-generated block randomization. Eligibility criteria are age (â Ľ14) years) and arsenic concentration in the household tube well (â Ľ100 ppb). In this double-blind study, one group will eat high-selenium lentils grown in western Canada; the other will consume low-selenium lentils grown in Idaho, USA. Each participant will consume 65 g of lentils each day for 6 months. At the onset, midterm, and end of the trial, blood, urine and stool, plus hair (day 1 and at 6 months only) samples will be collected and a health examination conducted including assessment of acute lung inflammation, body mass and height, and blood pressure. The major outcome will be arsenic excretion in urine and feces, as well as arsenic deposition in hair and morbidity outcomes as assessed by a biweekly questionnaire. Secondary outcomes include antioxidant status, lipid profile, lung inflammation status, and blood pressure. Discussion Selenium pills as a treatment for arsenic exposure are costly and inconvenient, whereas a whole food approach to lower the toxic burden of arsenic may be a practical remedy for Bangladeshi people while efforts to provide safe drinking water are continuing. If high-selenium lentils prove to be effective in counteracting arsenic toxicity, agronomic partnerships between Canada and Bangladesh will work to improve the selenium content of the Bangladeshi-grown lentil crops. Results will be presented to the community to promote informed food choices, which may include increasing selenium in their diet. Trial registration ClinicalTrials.gov NCT02429921
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.062 | 0.008 |
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".