Hypothalamic Protein Kinase C Regulates Glucose Production
Bibliographic record
Abstract
OBJECTIVE: A selective rise in hypothalamic lipid metabolism and the subsequent activation of SUR1/Kir6.2 ATP-sensitive K(+) (K(ATP)) channels inhibit hepatic glucose production. The mechanisms that link the ability of hypothalamic lipid metabolism to the activation of K(ATP) channels remain unknown. RESEARCH DESIGN AND METHODS: To examine whether hypothalamic protein kinase C (PKC) mediates the ability of central nervous system lipids to activate K(ATP) channels and regulate glucose production in normal rodents, we first activated hypothalamic PKC in the absence or presence of K(ATP) channel inhibition. We then inhibited hypothalamic PKC in the presence of lipids. Tracer-dilution methodology in combination with the pancreatic clamp technique was used to assess the effect of hypothalamic administrations on glucose metabolism in vivo. RESULTS: We first reported that direct activation of hypothalamic PKC via direct hypothalamic delivery of PKC activator 1-oleoyl-2-acetyl-sn-glycerol (OAG) suppressed glucose production. Coadministration of hypothalamic PKC-delta inhibitor rottlerin with OAG prevented the ability of OAG to activate PKC-delta and lower glucose production. Furthermore, hypothalamic dominant-negative Kir6.2 expression or the delivery of the K(ATP) channel blocker glibenclamide abolished the glucose production-lowering effects of OAG. Finally, inhibition of hypothalamic PKC eliminated the ability of lipids to lower glucose production. CONCLUSIONS: These studies indicate that hypothalamic PKC activation is sufficient and necessary for lowering glucose production.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".