Placental small extracellular vesicles from normal pregnancy and gestational diabetes increase insulin gene transcription and content in β cells
Bibliographic record
Abstract
Insulin secretion increases progressively during pregnancy to maintain normal maternal blood glucose levels. The placenta plays a crucial role in this process by releasing hormones and extracellular vesicles into the maternal circulation, which drive significant changes in pregnancy physiology. Placental extracellular vesicles, which are detectable in the plasma of pregnant women, have been shown to signal peripheral tissues and contribute to pregnancy-related conditions. While studies using murine models have demonstrated that extracellular vesicles can modulate insulin secretion in pancreatic islets, it remains unclear whether these effects translate to human biology. Understanding how placental signals enhance insulin synthesis and secretion from β cells could be pivotal in developing new therapies for diabetes. In our study, we isolated placental small extracellular vesicles from human placentae and utilised the human β cell line, EndoC-βH3, to investigate their effects on β-cell function in vitro. Our results indicate that human β cells internalise placental small extracellular vesicles, leading to enhanced insulin gene expression and increased insulin content within the β cells. Moreover, these vesicles up-regulated the expression of Annexin A1, a protein known to increase insulin content. This up-regulation of Annexin A1 holds promise as a potential mechanism by which placental small extracellular vesicles enhance insulin biosynthesis.
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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 | 0.000 | 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".