Maternal Genetic Risk Factors for Infant Zinc Deficiency
Bibliographic record
Abstract
Zinc is an essential constituent of catalytic sites of multiple enzymes. Since all of these enzymes are involved in cellular functions, zinc deficiency will have adverse effects on reproduction, normal growth and development. Zinc deficiency was reported in healthy term infants and attributed to a decrease in zinc secretion in maternal breast milk (BM). Two single nucleotide polymorphisms (SNPs) in the membrane transporter SLC30A2 (ZnT2) cause decreased zinc secretion into BM. Through bioinformatics analysis we identified 14 additional SNPs causing amino acid changes. We aim to identify the functional changes caused by these 14 SNPs, with the initial objective to identify possible cellular mislocalization. We subcloned the SLC30A2 open reading frames into the Gateway entry plasmid pDONR221 and introduced the 14 SNPs using site directed mutagenesis. During transfer into expression plasmid we tagged SLC30A2 with red and green fluorescent proteins (mCherry, tGFP). We transfected individual plasmids into mammary epithelial cells and observed cellular targeting using epifluorescent imaging. The most common variants locate to secreting endosomes in mammary epithelial cells. Incorrect targeting of SLC30A2 eliminates its role in zinc secretion, resulting in zinc depleted BM. Mothers carrying risk genotypes for infant zinc deficiency can be identified. Funded by CRC and CIHR
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.006 | 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".