Breast milk selenocystine as a biomarker for selenium intake in lactating women at differential geographical deficiency risk in China.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: A reliable biomarker for optimal selenium (Se) intake in lactating women is not currently available. METHODS AND STUDY DESIGN: Daily dietary Se intake in lactating women was calculated from a 24-hour meal record survey for over 3 days. Se levels in plasma and breast milk were measured through inductively coupled plasma mass spectrometry. Plasma selenoprotein P 1 levels and glutathione peroxidase 3 activity were measured using an enzyme-linked immunosorbent assay. Ultra-performance liquid chromatography-tandem mass spectrometry was used to analyze proteinaceous Se species in enzymatically digested breast milk. RESULTS: Dietary Se intakes of lactating women from Liangshan, Beijing, and Enshi were 41.6±21.2 ng/d, 51.1±22.6 ng/d, and 615±178 ng/d, respectively (p<0.05). The Se levels in the blood and breast milk were significantly associated with the dietary Se intake (p<0.05). The proteinaceous Se species in breast milk were SeMet and SeCys2. The levels of SeMet in the lactating women from Liangshan, Beijing, and Enshi were 3.31±2.44 ng Se/mL, 7.34±3.70 ng Se/mL, and 8.99±9.64 ng Se/mL, while that of SeCys2 were 13.7±12.0 ng Se/mL, 35.6±20.9 ng Se/mL, and 57.4±13.2 ng Se/mL, respectively. Notably, the concentration of SeCys2, the metabolite of unstable SeCys, reached a saturation platform, whereas no similar phenomenon were found for the total Se SeMet from Secontaining proteins. CONCLUSIONS: SeCys2 in breast milk is a potential biomarker for determining the optimal Se intake in lactating women.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".