Insulin modulates the electrical activity of subfornical organ neurons
Bibliographic record
Abstract
Insulin plays a crucial role in the regulation of energy balance. Within the central nervous system, hypothalamic nuclei such as the arcuate and ventromedial nuclei are targets of insulin; however, insulin may only access these nuclei after transport across the blood-brain barrier. Neurons of the subfornical organ are not protected by the blood-brain barrier and can rapidly detect and respond to circulating hormones such as leptin and ghrelin. Moreover, subfornical organ neurons form synaptic connections with hypothalamic control centers that regulate energy balance, including the arcuate and dorsomedial nuclei. However, it is unknown whether subfornical organ neurons respond to insulin. Using whole-cell current clamp, we examined the electrophysiological effects of insulin on rat subfornical organ neurons. Upon insulin application, 70% of neurons tested were responsive, with 33% of neurons tested (9/27) exhibiting hyperpolarization of membrane potential (-8.7 ± 1.7 mV) and 37% (10/27) exhibiting depolarization (10.5 ± 2.8 mV). Using pharmacological blockade, our data further indicate that the hyperpolarization was mediated by opening of KATP channels, whereas depolarization resulted from opening of Ih channels. These data are the first to show that insulin exerts a direct effect on the electrical activity of subfornical organ neurons and support the notion that the subfornical organ may act to communicate information on circulating satiety signals to homeostatic control centers.
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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) | 0.000 | 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 | 0.001 |
| 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".