Increased capacity to maintain glucose homeostasis in a transgenic mouse expressing human but not mouse growth hormone with developing high-fat diet-related insulin resistance, hepatic steatosis and adipose dysfunction
Bibliographic record
Abstract
The objective was to assess the potential differential effects of human versus mouse growth hormone in vivo, given that human unlike mouse growth hormone can bind prolactin as well as the growth hormone receptor. To this end, a transgenic CD-1 mouse expressing human but not mouse growth hormone was generated, and the phenotypes of male mice fed with a regular chow or high-fat diet were assessed. Pancreas and epididymal white adipose tissue gene expression and/or related function were targeted as the pancreas responds to both prolactin and growth hormone receptor signaling, and catabolic effects like lipolytic activity are more directly attributable to growth hormone and growth hormone receptor signaling. The resulting human growth hormone-expressing mice are smaller than wild-type CD-1 mice, despite higher body fat and larger adipocytes, but both mouse types grow at the same rate with similar bone densities. Unlike wild-type mice, there was no significant delay in glucose clearance in human growth hormone-expressing mice when assessed at 8 versus 24 weeks on a high-fat diet. However, both mouse types showed signs of hepatic steatosis that correlated with elevated prolactin but not growth hormone RNA levels. The larger adipocytes in human growth hormone-expressing mice were associated with modified leptin (higher) and adiponectin (lower) RNA levels. Thus, while limited to observations in the male, the human growth hormone-expressing mice exhibit signs of growth hormone insufficiency and adipocyte dysfunction as well as an initial resistance to the negative effects of high-fat diet on glucose clearance.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".