Editorial: Staving off gestational diabetes: Pancreatic islet adaptations and the extrinsic signals that drive them
Bibliographic record
Abstract
TopicStaving off gestational diabetes: Pancreatic islet adaptations and the extrinsic signals that drive them Preg nancy presents the maternal metabolism with the challenge of providing energy to the growing fetus whilst maintaining fuel homeostasis in the mother.A progressive increase in maternal insulin resistance over the course of gestation must be countered by increased insulin secretion to maintain normoglycemia.This increased insulin secretory capacity is met through functional changes in islet b-cells, including enhanced glucoseinduced insulin secretion and increased b-cell mass.A failure of the b-cells to sufficiently compensate for the metabolic demand in pregnancy may lead to maternal glucose intolerance, hyperglycaemia, and Gestational Diabetes Mellitus (GDM).The prevalence of GDM in pregnant has increased in parallel with Type 2 Diabetes, and between 13-25% of pregnancies are currently estimated to be affected by GDM (1-4).GDM represents a threat to both mother and child and is associated with complications such as high blood pressure, preeclampsia, preterm birth, and macrosomia.Furthermore, approximately half of women with GDM subsequently develop Type 2 Diabetes.Children of GDM pregnancies are at increased risk of obesity and developing Type 2 Diabetes in later life.Thus, the major healthcare concerns for individuals diagnosed with GDM and their offspring drive the clinical need to identify novel strategies for diagnosing and treating the condition more effectively.To do so, we feel it is critical to first better understand the cellular mechanisms involved in the b-cell adaptation to pregnancy and the maternal signals that drive the adaptive response, which motivated the development of this Research Topic.In response to our call for papers, we highlight 5 original research publications and two review articles accepted for publication.These encompass a diverse array of topics Frontiers in Endocrinology frontiersin.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.020 | 0.017 |
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".