I Prostanoid Receptor–Mediated Inflammatory Pathway Promotes Hepatic Gluconeogenesis Through Activation of PKA and Inhibition of AKT
Bibliographic record
Abstract
Nonsteroidal anti-inflammatory drugs (NSAIDs), including acetylsalicylic acid (ASA), improve glucose metabolism in diabetic subjects, although the underlying mechanisms remain unclear. In this study, we observed dysregulated expression of cyclooxygenase-2, prostacyclin biosynthesis, and the I prostanoid receptor (IP) in the liver's response to diabetic stresses. High doses of ASA reduced hepatic prostaglandin generation and suppressed hepatic gluconeogenesis in mice during fasting, and the hypoglycemic effect of ASA could be restored by IP agonist treatment. IP deficiency inhibited starvation-induced hepatic gluconeogenesis, thus inhibiting the progression of diabetes, whereas hepatic overexpression of IP increased gluconeogenesis. IP deletion depressed cAMP-dependent CREB phosphorylation and elevated AKT phosphorylation by suppressing PI3K-γ/PKC-ζ-mediated TRB3 expression, which subsequently downregulated the gluconeogenic genes for glucose-6-phosphatase (G6Pase) and phosphoenol pyruvate carboxykinase 1 in hepatocytes. We therefore conclude that suppression of IP modulation of hepatic gluconeogenesis through the PKA/CREB and PI3K-γ/PKC-ζ/TRB3/AKT pathways contributes to the effects of NSAIDs in diabetes.
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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) | 0.001 | 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".