Effects of maternal iron status on placental and fetal iron homeostasis
Bibliographic record
Abstract
Iron deficiency is common worldwide and is associated with adverse pregnancy outcomes. The increasing prevalence of indiscriminate iron supplementation during pregnancy also raises concerns about the potential adverse effects of iron excess. We examined how maternal iron status affects the delivery of iron to the placenta and fetus. Using mouse models, we documented maternal homeostatic mechanisms that protect the placenta and fetus from maternal iron excess. We determined that under physiological conditions or in iron deficiency, fetal and placental hepcidin did not regulate fetal iron endowment. With maternal iron deficiency, critical transporters mediating placental iron uptake (transferrin receptor 1 [TFR1]) and export (ferroportin [FPN]) were strongly regulated. In mice, not only was TFR1 increased, but FPN was surprisingly decreased to preserve placental iron in the face of fetal iron deficiency. In human placentas from pregnancies with mild iron deficiency, TFR1 was increased, but there was no change in FPN. However, induction of more severe iron deficiency in human trophoblast in vitro resulted in the regulation of both TFR1 and FPN, similar to what was observed in the mouse model. This placental adaptation that prioritizes placental iron is mediated by iron regulatory protein 1 (IRP1) and is important for the maintenance of mitochondrial respiration, thus ultimately protecting the fetus from the potentially dire consequences of generalized placental dysfunction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".