Maternal–Fetal Nutrient Transport in Pregnancy Pathologies: The Role of the Placenta
Bibliographic record
Abstract
Appropriate in utero growth is essential for offspring development and is a critical contributor to long-term health. Fetal growth is largely dictated by the availability of nutrients in maternal circulation and the ability of these nutrients to be transported into fetal circulation via the placenta. Substrate flux across placental gradients is dependent on the accessibility and activity of nutrient-specific transporters. Changes in the expression and activity of these transporters is implicated in cases of restricted and excessive fetal growth, and may represent a control mechanism by which fetal growth rate attempts to match availability of nutrients in maternal circulation. This review provides an overview of placenta nutrient transport with an emphasis on macro-nutrient transporters. It highlights the changes in expression and activity of these transporters associated with common pregnancy pathologies, including intrauterine growth restriction, macrosomia, diabetes and obesity, as well as the potential impact of maternal diet. Molecular signaling pathways linking maternal nutrient availability and placenta nutrient transport are discussed. How sexual dimorphism affects fetal growth strategies and the placenta's response to an altered intrauterine environment is considered. Further knowledge in this area may be the first step in the development of targeted interventions to help optimize fetal growth.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".