Role of glycerol-3-phosphate phosphatase (G3PP) in pancreatic β-cells and liver
Bibliographic record
Abstract
Chronic nutritional excess results in metabolic disorders such as obesity, type 2 diabetes and fatty liver disease.Hyperglycemia is a frequent characteristic of these disorders; and chronic hyperglycemia can damage different cells and tissues including the pancreatic β-cells and liver cells through a process called glucotoxicity.Detoxification pathways that eliminate excess glucose carbons can help protect cells and tissues from damage.The glycerolipid/free fatty acid (GL/FFA) cycle is a critical detoxification pathway that plays an important role in the overall regulation of glucose and lipid metabolism.Glycerol-3-phosphate (Gro3P), which is formed from glucose during glycolysis, is at the crossroads of glucose and lipid metabolism and one of the starting substrates for the GL/FFA cycle.Gro3P is hydrolyzed by glycerol-3-phosphate phosphatase (G3PP) to glycerol, which suggests that G3PP could be an important metabolic regulator and raises the possibility that defective G3PP activity could lead to metabolic dysfunction.We have shown recently that G3PP, by regulating cytosolic Gro3P levels, can play a role in the control of glycolysis, glucose oxidation, cellular redox and ATP production, gluconeogenesis and glycerolipid synthesis in β-cells and hepatocytes in vitro.In this thesis I studied the potential role of G3PP in β-cells and liver in vivo and viewing G3PP as a potential
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".