The actions of ghrelin in the paraventricular nucleus: energy balance and neuroendocrine implications
Bibliographic record
Abstract
Ghrelin is a peptide mainly produced and secreted by the stomach. Since its discovery, the impact of ghrelin on the regulation of food intake has been the most studied function of this hormone; however, ghrelin affects a wide range of physiological systems, many of which are controlled by the hypothalamic paraventricular nucleus (PVN). Several pathways may mediate the effects of ghrelin on PVN neurons, such as direct or indirect effects mediated by circumventricular organs and/or the arcuate nucleus. The ghrelin receptor is expressed in PVN neurons, and the peripheral or intracerebroventricular administration of ghrelin affects PVN neuronal activity. Intra-PVN application of ghrelin increases food intake and decreases fat oxidation, which chronically contribute to the increased adiposity. Additionally, ghrelin modulates the neuroendocrine axes controlled by the PVN, increasing the release of vasopressin and oxytocin by magnocellular neurons and corticotropin-releasing hormone by neuroendocrine parvocellular neurons, while possibly inhibiting the release of thyrotropin-releasing hormone. Thus, the PVN is an important target for the actions of ghrelin. Our review discusses the mechanisms of ghrelin actions in the PVN, and its potential implications for energy balance, neuroendocrine, and integrative physiological control.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".