Gene expression profiles suggest iron transport pathway in human lactating epithelialcell
Bibliographic record
Abstract
Early determination of the factors predisposing an infant to iron deficiency during breastfeeding may enable timely intervention via dietary supplementation. However, the molecular background of iron excretion into breast milk has not been determined in humans. We determined the expression of known iron transporters in the lactating and non‐lactating human and mouse mammary epithelial cell by RT‐PCR to deduce which transporters are responsible for iron excretion into breast milk. The mRNA extracted from breast milk was mainly from epithelial and macrophage cells. In addition, there were only two iron transporters (TFRC and DMT‐1) present in both the lactating human and mouse epithelial cell. SLC40A1, the gene of the only identified cellular iron exporter (FPN), was not expressed in lactating human cells but was previously reported to be expressed in the rat epithelial cell. Our findings indicate that, rat, the only reported model for the iron secreting pathway may not be the best corresponding model for the iron transport system in the human mammary gland. We propose a potential iron transport pathway in lactating human mammary epithelial cell based on the gene expression profiles of the mRNA extracted from breast milk. Supported by 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.001 |
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".