Protective roles of hepatic GABA signaling in acute liver injury of rats
Bibliographic record
Abstract
γ-Aminobutyric acid (GABA) is produced by various cells through the catalytic activity of glutamic acid decarboxylase (GAD). Activation of type-A GABA receptor (GABA A R) inhibits stem cell proliferation but protects differentiated cells from injures. The present study investigated hepatic GABA signaling system and the role of this system in liver physiology and pathophysiology. RT-PCR and immunoblot assays identified GAD and GABA A R subunits in rat livers and in HepG2 and Clone 9 hepatocytes. Patch-clamp recording detected GABA-induced currents in Clone 9 hepatocytes and depolarization in WITT cholangiocytes. The function of hepatic GABA signaling system in rats was examined using models of d-galactosamine (GalN)-induced acute hepatocytic injury in vivo and in vitro. The expression of GAD increased whereas GABA A R subunits decreased in the liver of GalN-treated rats. Remarkably, treating rats with GABA or the GABA A R agonist muscimol, but not the GABA B R agonist baclofen, protected hepatocytes against GalN toxicity and improved liver function. In addition, muscimol treatment decreased the formation of pseudobile ductules and the enlargement of hepatocytic canaliculi in GalN-treated rats. Our results revealed that a complex GABA signaling system exists in the rat liver. Activation of this intrahepatic GABAergic system protected the liver against toxic injury. NEW & NOTEWORTHY Auto- and paracrine GABAergic signaling systems exist in the rat hepatocytes and cholangiocytes. Activation of GABA signaling protects liver function from d-galactosamine injury by reducing toxic impairment of hepatocytes and by decreasing cholangiocyte proliferation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".