Disruption of growth hormone receptor gene causes diminished pancreatic islet size and increased insulin sensitivity in mice
Bibliographic record
Abstract
Growth hormone, acting through its receptor (GHR), plays an important role in carbohydrate metabolism and in promoting postnatal growth. GHR gene-deficient (GHR(-/-)) mice exhibit severe growth retardation and proportionate dwarfism. To assess the physiological relevance of growth hormone actions, GHR(-/-) mice were used to investigate their phenotype in glucose metabolism and pancreatic islet function. Adult GHR(-/-) mice exhibited significant reductions in the levels of blood glucose and insulin, as well as insulin mRNA accumulation. Immunohistochemical analysis of pancreatic sections revealed normal distribution of the islets despite a significantly smaller size. The average size of the islets found in GHR(-/-) mice was only one-third of that in wild-type littermates. Total beta-cell mass was reduced 4.5-fold in GHR(-/-) mice, significantly more than their body size reduction. This reduction in pancreatic islet mass appears to be related to decreases in proliferation and cell growth. GHR(-/-) mice were different from the human Laron syndrome in serum insulin level, insulin responsiveness, and obesity. We conclude that growth hormone signaling is essential for maintaining pancreatic islet size, stimulating islet hormone production, and maintaining normal insulin sensitivity and glucose homeostasis.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".