Metabolic Adaptations to Pregnancy in Healthy and Gestational Diabetic Pregnancies: The Pancreas - Placenta Axis
Bibliographic record
Abstract
Normal pregnancy is associated with increased insulin resistance as a metabolic adaptation to the nutritional demands of the placenta and fetus, and this is amplified in obese mothers. Insulin resistance is normally compensated for by an adaptive increase in pancreatic β-cell mass together with enhanced glucose-stimulated insulin release. Placentally-derived hormones and growth factors are central to the altered pancreatic morphology and function. A failure of β-cells to undergo adaptive change after the first trimester has been linked with gestational diabetes. In the pregnant mouse, an increase in β-cell replication contributes to a 2-3-fold increase in mass peaking in late gestation, depending on the proliferation of existing β-cells, the differentiation of resident progenitor β-cells, or islet cell transdifferentiation. Using mouse models and human studies placenta- and islet of Langerhans-derived molecules have been identified that are likely to contribute to the metabolic adaptations to pregnancy and whose physiology is altered in the obese, glucose-intolerant mother. Maternal obesity during pregnancy can create a pro-inflammatory environment that can disrupt the response of the β-cells to the endocrine signals of pregnancy and limit the adaptive changes in β-cell mass and function, resulting in an increased risk of gestational diabetes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".