Functional interaction between Ghrelin and GLP-1 regulates feeding through the vagal afferent system
Bibliographic record
Abstract
Abstract The gastrointestinal tract transmits feeding-regulatory signals to the brain via neuronal and hormonal pathways. Here we studied the interaction between the orexigenic gastric peptide, ghrelin, and the anorectic intestinal peptide, glucagon-like peptide 1 (GLP-1), in terms of feeding regulation via the vagal afferents. GLP-1 preadministration 30 min before ghrelin administration to rats and mice abolished ghrelin-induced food intake, while ghrelin preadministration abolished the anorectic effect of GLP-1. Ghrelin preadministration suppressed GLP-1-induced Fos expression in the nodose ganglia (NG). Electrophysiological assessment confirmed that the initially administered peptide abolished the vagal afferent electrical alteration induced by the subsequently administered peptide. Both the growth hormone secretagogue receptor (GHSR) and the GLP-1 receptor (GLP-1R) are co-localised in a major proportion of NG neurons that innervate the stomach. In these Ghsr + Glp1r + neurons, ghrelin preadministration abolished the GLP-1-induced calcium response. Ghrelin generated a hyperpolarising current and GLP-1 generated a depolarising current in isolated NG neurons in a patch-clamp experiment. Ghrelin and GLP-1 potently influenced each other in terms of vagally mediated feeding regulation. This peptidergic interaction allows for fine control of the electrophysiological properties of NG neurons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".