Identification of the Metabolic Enzyme Involved Morusin Metabolism and Characterization of Its Metabolites by Ultraperformance Liquid Chromatography Quadrupole Time‐of‐Flight Mass Spectrometry (UPLC/Q‐TOF‐MS/MS)
Bibliographic record
Abstract
Morusin, the important active component of a traditional Chinese medicine, Morus alba L., has been shown to exhibit many vital pharmacological activities. In this study, six recombinant CYP450 supersomes and liver microsomes were used to perform metabolic studies. Chemical inhibition studies and screening assays with recombinant human cytochrome P450s were also used to characterize the CYP450 isoforms involved in morusin metabolism. The morusin metabolites identified varied greatly among different species. Eight metabolites of morusin were detected in the liver microsomes from pigs (PLMs), rats (RLMs), and monkeys (MLMs) by LC‐MS/MS and six metabolites were detected in the liver microsomes from humans (HLMs), rabbits (RAMs), and dogs (DLMs). Four metabolites (M1, M2, M5, and M7) were found in all species and hydroxylation was the major metabolic transformation. CYP1A2, CYP2C9, CYP2D6, CYP2E1, CYP3A4, and CYP2C19 contributed differently to the metabolism of morusin. Compared to other CYP450 isoforms, CYP3A4 played the most significant role in the metabolism of morusin in human liver microsomes. These results are significant to better understand the metabolic behaviors of morusin among various species.
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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".