Gigantol Improves Cholesterol Metabolism and Progesterone Biosynthesis in MA-10 Leydig Cells
Bibliographic record
Abstract
In aging males, androgen production by testicular Leydig cells decreases at a rate of approximately 1% per year. Phenolic compounds may enhance testosterone biosynthesis and delay the onset of male hypogonadism. Gigantol is a bibenzyl compound isolated from several types of orchids of the genus Dendrobium. This compound has various biological activities, including antioxidant activity. However, its capacity to regulate gene expression and steroid production in testicular Leydig cells has never been evaluated. We investigated the effect of gigantol on MA-10 Leydig cells' gene expression using an RNA-Seq approach. To further investigate the structure-function relationship of the hydroxy-methoxyphenyl moiety of gigantol, experiments were also performed with ferulic acid and isoferulic acid. According to transcriptomic analysis, all genes coding for cholesterol biosynthesis-related enzymes are increased in response to gigantol treatment, resulting in increased lipid droplets accumulation. Moreover, treatments with 10 μM gigantol increased StAR protein levels and progesterone production from MA-10 Leydig cells. However, neither ferulic acid nor isoferulic acid influenced StAR protein synthesis and progesterone production in MA-10 Leydig cells. Thus, our findings indicate that gigantol improves cholesterol and steroid biosynthesis within testicular Leydig 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) | 0.001 | 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 | 0.001 |
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".