Do POMC neurons have a sweet tooth for leptin? Special issue: Role of nutrients in nervous control of energy balance
Bibliographic record
Abstract
Coordinated detection of changes in metabolic state by the nervous system is fundamental for survival. Hypothalamic pro-opiomelanocortin (POMC) neurons play a critical role in integrating metabolic signals, including leptin levels. They also coordinate adaptative responses and thus represent an important relay in the regulation of energy balance. Despite a plethora of work documenting the effects of individual hormones, nutrients, and neuropeptides on POMC neurons, the importance for crosstalk and additive effects between such signaling molecules is still underexplored. The ability of the metabolic state and the concentrations of nutrients, such as glucose, to influence leptin's effects on POMC neurons appears critical for understanding the function and complexity of this regulatory network. Here, we summarize the current knowledge on the effects of leptin on POMC neuron electrical excitability and discuss factors potentially contributing to variability in these effects, with a particular focus on the mouse models that have been developed and the importance of extracellular glucose levels. This review highlights the importance of the metabolic "environment" for determining hypothalamic neuronal responsiveness to metabolic cues and for determining the fundamental effects of leptin on the activity of hypothalamic POMC neurons.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".