The Role of Central Glucagon-Like Peptide-1 in Mediating the Effects of Visceral Illness: Differential Effects in Rats and Mice
Bibliographic record
Abstract
In rats, central administration of glucagon-like peptide-1 (GLP-1) elicits symptoms of visceral illness like those caused by the toxin lithium chloride (LiCl), including anorexia, conditioned taste aversion (CTA) formation, and neural activation in the hypothalamus and hindbrain including activation of brainstem preproglucagon cells. Most compellingly, pharmacological antagonists of the GLP-1 receptor (GLP-1R) block several effects of LiCl in rat. The major goal of these experiments was to further test the hypothesis that the central nervous system GLP-1 system is critical to the visceral illness actions of LiCl by using mice with a targeted disruption of the only described GLP-1R. First, we observed that, like the rat, LiCl activates preproglucagon neurons in wild-type mice. Second, GLP-1R -/- mice demonstrated normal anorexic and CTA responses to LiCl. To test the possibility that alternate GLP-1Rs mediate aversive effects, we examined the ability of GLP-1 to produce a CTA in GLP1R -/- mice. Although lateral ventricular GLP-1 produced a CTA in wild-type mice, it did not produce a CTA in GLP-1R -/- mice. Furthermore, the same GLP-1R antagonist that can block the aversive effects of LiCl in the rat failed to do so in the mouse. These results support the conclusion that in mouse, unlike in rat, GLP-1R signaling is not required for the visceral illness response to LiCl. Such species differences are an important consideration when comparing results from rat and mouse studies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".