Harmine and Harmaline Downregulate TCDD-Induced Cyp1a1 in the Livers and Lungs of C57BL/6 Mice
Bibliographic record
Abstract
We previously demonstrated that Peganum harmala L. extract and its main active constituents, harmine and harmaline inhibit the 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)-mediated induction of the carcinogen-activating enzyme, Cyp1a1, in vitro. However, the effect of both alkaloids on Cyp1a1 in vivo has not been investigated. Therefore, the aim of this study is to examine the effect of harmine and harmaline on TCDD-mediated induction of Cyp1a1 in mice livers and lungs. C57BL/6 male mice were distributed into four groups (n = 6). First group received vehicle, while the second group received TCDD (i.p.). The third and fourth groups received either harmine or harmaline (i.p.) × 3 times along with TCDD one time with the mid dose of harmine and harmaline. All mice were sacrificed after 14 h from TCDD injection, and livers and lungs were isolated. The effect of harmine and harmaline on TCDD-mediated induction of Cyp1a1 mRNA, protein, and activity levels was determined using real-time PCR, Western blot analysis, and 7-ethoxyresurofin as a substrate, respectively. Our results showed that harmine and harmaline significantly decreased the TCDD-mediated induction of Cyp1a1 in both the livers and lungs. We concluded that harmine and harmaline are promising candidate to inhibit TCDD-mediated induction of Cyp1a1 in mice hepatic and extrahepatic tissues.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".