Maternal Iron Deficiency Alters Trophoblast Differentiation and Placental Development in Rat Pregnancy
Bibliographic record
Abstract
Iron deficiency, which occurs when iron demands chronically exceed intake, is prevalent in pregnant women. Iron deficiency during pregnancy poses major risks for the baby, including fetal growth restriction and long-term health complications. The placenta serves as the interface between a pregnant mother and her baby, and it ensures adequate nutrient provisions for the fetus. Thus, maternal iron deficiency may impact fetal growth and development by altering placental function. We used a rat model of diet-induced iron deficiency to investigate changes in placental growth and development. Pregnant Sprague-Dawley rats were fed either a low-iron or iron-replete diet starting 2 weeks before mating. Compared with controls, both maternal and fetal hemoglobin were reduced in dams fed low-iron diets. Iron deficiency decreased fetal liver and body weight, but not brain, heart, or kidney weight. Placental weight was increased in iron deficiency, due primarily to expansion of the placental junctional zone. The stimulatory effect of iron deficiency on junctional zone development was recapitulated in vitro, as exposure of rat trophoblast stem cells to the iron chelator deferoxamine increased differentiation toward junctional zone trophoblast subtypes. Gene expression analysis revealed 464 transcripts changed at least 1.5-fold (P < 0.05) in placentas from iron-deficient dams, including altered expression of genes associated with oxygen transport and lipoprotein metabolism. Expression of genes associated with iron homeostasis was unchanged despite differences in levels of their encoded proteins. Our findings reveal robust changes in placentation during maternal iron deficiency, which could contribute to the increased risk of fetal distress in these pregnancies.
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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) | 0.001 | 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".