Geranylgeraniol Induces PPARγ Expression and Enhances the Biological Effects of a PPARγ Agonist in Adipocyte Lineage Cells
Bibliographic record
Abstract
BACKGROUND: The global incidence of diabetes mellitus (DM) has risen precipitously, even in middle- and low-income countries. Peroxisome proliferator-activated receptor γ (PPARγ) plays an important role in the control of cellular glucose metabolism. Activation of PPARγ beneficially results in increased insulin sensitivity. However, the expression of PPARγ is reduced by obesity and several nutritional factors. Here we examined the effect of geranylgeraniol (GGOH), a bioactive compound found naturally in fruits, vegetables, and grains, on the expression and activation of PPARγ. MATERIALS AND METHODS: C3H10T1/2 mouse embryonic fibroblasts and 3T3-L1 pre-adipocytes were used as in vitro models of adipocyte differentiation and function. Quantitative reverse-transcriptase polymerase chain reaction, western blotting, Oil Red O staining, and luciferase assay were performed to respectively assess mRNA expression, protein levels, lipid droplet formation and transcriptional activity. RESULTS: GGOH increased the expression of PPARγ in adipocyte lineage cells. GGOH also enhanced adipogenesis induced by rosiglitazone, a thiazolidinedione class PPARγ agonist. CONCLUSION: GGOH induces PPARγ expression and enhances the biological effects of a PPARγ agonist in adipocyte lineage cells.
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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) | 0.002 | 0.001 |
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".