<i>Peganum harmala</i>L. is a Candidate Herbal Plant for Preventing Dioxin Mediated Effects
Bibliographic record
Abstract
Dioxins are widespread environmental contaminants that have been linked with a variety of deleterious effects on human health including increased cancer rates. The detrimental effects of 2,3,7,8-tetrachlorodibenzo- P-dioxin (TCDD, one of the most common environmental dioxins) are mediated via the aryl hydrocarbon receptor (AhR). AhR is a transcription factor that regulates the expression of the carcinogen-activating enzyme, cytochrome P450 1a1 (Cyp1a1). In the present study, we examined the ability of the methanolic extract of Peganum harmala L. (Zygophyllaceae) fruiting tops to affect TCDD-activated AhR-mediated signal transduction in mouse hepatoma Hepa 1c1c7 cells. Our results showed that Peganum harmala extract significantly inhibited the TCDD-mediated induction of Cyp1a1 at mRNA, protein, and activity levels. A similar pattern of inhibition at the catalytic activity level was observed with the other AhR ligands tested. The ability of the extract to inhibit Cyp1a1 was strongly correlated with its ability to inhibit AhR-dependent luciferase activity and electrophoretic mobility shift assays. Harmine and harmaline were found to be the dominant components of the plant extract with a relative abundance of 7 and 4.85 % (w/w), respectively. In addition, both of the active alkaloids showed an inhibitory effect on TCDD-induced Cyp1a1 activity level. We concluded that Peganum harmala L. can interfere with AhR ligands-mediated effects.
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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) | 0.002 | 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".