Effect of geraniol on fatty-acid and mevalonate metabolism in the human hepatoma cell line Hep G2
Bibliographic record
Abstract
Monoterpenes have multiple pharmacological effects on the metabolism of mevalonate. Geraniol, a dietary monoterpene, has in vitro and in vivo anti-tumor activity against several cell lines. We have studied the effects of geraniol on growth, fatty-acid metabolism, and mevalonate metabolism in the human hepatocarcinoma cell line Hep G2. Up to 100 micromol geraniol/L inhibited the growth rate and 3-hydroxymethylglutaryl coenzyme A reductase (HMG-CoA) reductase activity of these cells. At the same concentrations, it increased the incorporation of cholesterol from the medium in a dose-dependent manner. Geraniol-treated cells incorporated less 14C-acetate into nonsaponifiable lipids, inhibiting its incorporation into cholesterol but not into squalene and lanosterol. This is indicative of an inhibition in cholesterol synthesis at a step between lanosterol and cholesterol, a fact confirmed when cells were incubated with 3H-mevalonate. The incorporation of 3H-mevalonate into protein was also inhibited, whereas its incorporation into fatty acid increased. An inhibition of delta5 desaturase activity was demonstrated by the inhibition of the conversion of 14C-dihomo-gamma-linolenic acid into arachidonic acid. Geraniol has multiple effects on mevalonate and lipid metabolism in Hep G2 cells, affecting cell proliferation. Although mevalonate depletion is not responsible for cellular growth, it affects cholesterogenesis, protein prenylation, and fatty-acid metabolism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".