Spectrophotometric Analysis of Human CY P2El -Catalyzed pNitrophenol Hydroxylation
Bibliographic record
Abstract
CYP2E1 is expressed in adult ( 1 , 2 ) and fetal ( 3 ) human liver in addition to extrahepatic tissues such as lung and placenta ( 4 ). Treatment of primary cultures of human hepatocytes with ethanol induces CYP2E1 protein ( 5 ) and this is consistent with the finding that hepatic CYP2El protein ( 6 ) and mRNA ( 7 ) amounts are increased in alcoholics. Although only a few drugs (e.g., acetaminophen [ 8 ]) have been identified as substrates for CYP2E1, many low molecular weight procarcinogens are activated by this P450 ( 9 ). Chlorzoxazone 6-hydroxylation ( 19 – 12 ), N-nitrosodimethylamine N-demethylation ( 11 , 13 , 14 ), and p-nitrophenol hydroxylation ( 12 , 14 ) reactions can be used to measure the catalytic activity of cDNA-expressed CYP2El. Each of these activities is also frequently used as an enzyme-selective catalytic monitor for human hepatic CYP2El ( see Notes 1 and 2 ). Experiments with inhibitory antiCYP2E1 antibodies and CYP2E1-selective chemical inhibitors suggest that CYP2E1 contributes extensively to these activities in human liver microsomes ( 9 , 15 – 18 ). Recently, lauric acid 11 -hydroxylation was identified as an alternative probe for human hepatic CY2El ( 19 ). However, an advantage of using p-nitrophenol hydroxylation to measure CYP2E1 activity is that the formation of p-nitrocatechol can be measured by a simple spectrophotometric assay, although high-performance liquid chromatographic (HPLC) assays have also been developed to quantify thep-nitrocatechol metabolite ( 20 , 21 ). This chapter describes a modification of a spectrophotometric method ( 22 ) for the determination of p-nitrophenol hydroxylase activity. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".