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Record W3206242311 · doi:10.1787/56e9bbf0-en

Adverse Outcome Pathway on Cyp2E1 activation leading to liver cancer

2021· report· en· W3206242311 on OpenAlexaff
Francina Webster, Iain B. Lambert, Carole L. Yauk

Bibliographic record

VenueOECD series on adverse outcome pathways · 2021
Typereport
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacogenetics and Drug Metabolism
Canadian institutionsCarleton UniversityUniversity of OttawaHealth Canada
Fundersnot available
KeywordsCYP2E1Oxidative stressReactive oxygen speciesChemistryCytochrome P450Reactive intermediateLiver injuryCytotoxicityBiochemistryCancer researchCell biologyPharmacologyMetabolismBiologyCatalysisIn vitro

Abstract

fetched live from OpenAlex

The present AOP describes the prolonged activation of Cyp2E1 resulting in liver cancer. Cyp2E1 is a cytochrome P450 mono-oxygenase that bioactivates over 85 substrates, thereby creating electrophilic metabolites and oxidative stress. Mono-oxygenation of these substrates to their reactive metabolites, and the accompanying oxidative stress produced during metabolism, pose health risks because they lead to hepatotoxicity and, often, to liver cancer. The MIE occurs when Cyp2E1 binds a substrate. The Cyp2E1 catalytic cycle is prone to decoupling, which produces oxidative stress (KE1), and mono-oxidation of substrates produces reactive metabolites. Both reactive oxygen species and metabolites cause cytotoxicity (KE2). However, following injury, the liver is able to regenerate itself through an increase in cellular proliferation (KE3). Under conditions of chronic activation of Cyp2E1, excessive chronic increases in levels of reactive oxygen species and cell death, and subsequent dysregulated cellular proliferation, leads to tumour formation (AO).

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.005

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.

Opus teacher head0.306
GPT teacher head0.472
Teacher spread0.166 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueOECD series on adverse outcome pathwaysSame topicPharmacogenetics and Drug MetabolismFrench-language works237,207