Selenoneine is a major selenium species in beluga skin and red blood cells of Inuit from Nunavik
Bibliographic record
Abstract
Nunavimmiut (Inuit of Nunavik, Northern Quebec, Canada) exhibit a high selenium (Se) status because of their frequent consumption of marine mammal foods. Indirect evidence from our previous studies had suggested that selenoneine - a novel selenocompound - may be accumulating in the blood of Nunavimmiut. We used a liquid-chromatography/inductively coupled tandem mass spectrometry (LC-ICP-MS/MS) method to measure concentrations of selenoneine and its methylated metabolite Se-methylselenoneine in archived red blood cells (RBC) obtained from 210 Nunavimmiut living in communities along the Hudson Strait, where marine mammal hunting and consumption are most frequent in Nunavik. This method was adapted to quantify selenoneine and its methylated metabolite in beluga mattaaq, an Inuit delicacy consisting of the skin with the underlying layer of fat and the major dietary source of Se for Nunavimmiut. Total selenium concentration was also measured in RBC and beluga mattaaq samples by isotope dilution ICP-MS/MS. The median selenoneine concentration in RBC was 413 μg Se/L (range = 3.20-3230 μg Se/L), representing 54% (median) of total Se content (range = 1.6-91%). Quantification of selenoneine in five beluga mattaaq samples (skin layer) from Nunavik revealed a median concentration of 1.8 μg Se/g wet wt (range = 1.2-7.4 μg Se/g), constituting 54% (median) of the total Se content (range = 44-74%). Se-methylselenoneine was also detected in Inuit RBC but not in beluga mattaaq, suggesting that selenoneine undergoes methylation in humans. Selenoneine may protect Nunavimmiut from methylmecury toxicity by increasing its demethylation in RBC and in turn decreasing its distribution to target organs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".