Interaction Between Orexins and the Mesolimbic System for Overriding Satiety
Bibliographic record
Abstract
In North American society, it is all too common for the intake of calories to outweigh an individual's energy demands. Such over-consumption where high-energy foods are readily available undoubtedly contributes to the growing problem of obesity. Palatable food stimulates brain circuits similar to those that mediate behavioral responses to drugs of abuse, which may underlie the continuation of food intake long after energy requirements are met. Among the brain areas implicated in reward and food intake, the lateral hypothalamus (LH) has long been recognized as a common region involved in both. It has been suggested that orexin neurons that are expressed exclusively within and adjacent to the LH comprise a major cellular substrate for the functioning of the LH. Here, we review the idea that the orexin neuropeptides play a key role in the rewarding aspects of food intake through interactions with both peripheral and central signals reflecting current energy stores as well as the classic reward pathway--the mesolimbic dopamine system. Furthermore, a possible heterogeneity of orexin neurons is discussed. Uncovering orexin's role in food reinforcement may provide insight into hyperphagia and obesity. In addition, the idea that food intake and substance abuse involve similar brain circuitry suggests potential for a single treatment aiding both obesity and addiction.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".