Modulation of PXR and VDR Pathways by 1,25D and Bioactive Compounds of St. John's Wort and Soy in HepG2 Cells
Bibliographic record
Abstract
As nuclear receptor, PXR and VDR have functional similarities by altering gene transcription in response to diet‐derived ligands. Bioactive compounds of St. John's Wort and soy have been targeted as potential nuclear receptor ligands. Although PXR and drug metabolism pathways regulated by PXR (MDR‐1) have been shown to be active in liver cells, the vitamin D pathway has not been well studied in this model. Our hypothesis is that both PXR and VDR pathways are active in hepatic cells and may be modulated by bioactive compounds. We examined the in vitro effects of 24h exposure of hypericin, hyperforin, genistein and 1,25‐vitamin D 3 (1,25‐D) on HepG2 cells. Using RT‐PCR, we measured mRNA levels of nuclear receptors (PXR and VDR) and metabolic genes (MDR‐1 and CYP24). Preliminary results suggest that within 24h, cells treated with hypericin, hyperforin and genistein showed an inhibition of MDR‐1 and PXR expression compared to control (time 0). 1,25‐D induced MDR‐1 and PXR during the first 12h, followed by an inhibition between 12h and 24h. All treatments caused an inhibition of VDR and an activation of CYP24 transcripts by 24h. Taken together, these observations suggest that both PXR and VDR pathways can be modulated by bioactive compounds, indicating that they may be active in HepG2 cells. Western blot analysis is under investigation to assess gene function.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".