Metabolism of 3-Methylindole by Porcine Liver Microsomes: Responsible Cytochrome P450 Enzymes
Bibliographic record
Abstract
The role of different cytochrome P450 enzymes on the metabolism of 3-methylindole (3MI) was investigated using selective chemical inhibitors. Eight chemical inhibitors of P450 enzymes were screened for their inhibitory specificity towards 3MI metabolism in porcine microsomes: alpha-naphthoflavone (CYP1A1/2), 8-methoxypsoralen (CYP2A6), menthofuran (CYP2A6), diethyldithiocarbamate (CYP2A6), 4-methylpyrazole (CYP2E1), sulphaphenazole (CYP2C9), quinidine (CYP2D6), and troleandomycin (CYP3A4). The production of 3MI metabolites was only affected by the presence of inhibitors of CYP2A6 and CYP2E1 in the microsomal incubations. In a second experiment, a set of porcine microsomes (n = 30) was analyzed for CYP2A6 content by protein immunoblot analysis and for their coumarin 7-hydroxylation activity (CYP2A6 activity). Both CYP2A6 content and enzymatic activity were found to be highly and negatively correlated with 3MI fat content. The results of the present study indicate that the CYP2A6 porcine ortholog plays an important role in the metabolism of 3MI and that measurement of CYP2A6 levels and/or activity could be a useful marker for 3MI-induced boar taint.
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) | 0.001 | 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".