Regulation of islet function and gene expression by prolactin during pregnancy
Bibliographic record
Abstract
Abstract Pancreatic islets adapt to insulin resistance of pregnancy by up regulating β-cell proliferation and increase insulin secretion. Previously, we found that prolactin receptor (Prlr) signaling is important for this process, as heterozygous prolactin receptor-null (Prlr +/− ) mice are glucose intolerant, had a lower number of β cells and lower serum insulin levels than wild type mice during pregnancy. However, since Prlr expression is ubiquitous, to determine its β-cell specific effects, we generated a transgenic mouse with a floxed Prlr allele under the control of an inducible promoter, allowing conditional deletion of Prlr from β cells in adult mice. In this study, we found that β-cell-specific Prlr reduction resulted in elevated blood glucose during pregnancy. Similar to our previous finding in mouse with global Prlr reduction, β-cell-specific Prlr loss led to a lower β-cell mass and a lower in vivo insulin level during pregnancy. However, these islets do not have an intrinsic insulin secretion defect when tested in vitro. Interestingly, when we compared the islet gene expression profile, using islets isolated from mice with global versus β-cell-specific Prlr reduction, we found some important differences in genes that regulate apoptosis and insulin secretion. This suggests that Prlr has both cell-autonomous and non-cell-autonomous effect on β cells, beyond its regulation of pro-proliferative genes.
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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".