MétaCan
Menu
Back to cohort
Record W4386069873 · doi:10.3390/antiox12091653

Oxidative Stress in Liver Pathophysiology and Disease

2023· review· en· W4386069873 on OpenAlexafffund
Abdolamir Allameh, Reyhaneh Niayesh‐Mehr, Azadeh Aliarab, Giada Sebastiani, Kostas Pantopoulos

Bibliographic record

VenueAntioxidants · 2023
Typereview
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsJewish General HospitalMcGill UniversityMcGill University Health Centre
FundersCanadian Institutes of Health Research
KeywordsOxidative stressFatty liverSteatohepatitisAlcoholic liver diseaseCirrhosisLiver diseaseLiver injuryMedicineHepatocellular carcinomaPathophysiologyReactive oxygen speciesHepatitisViral hepatitisDiseaseImmunologyBiologyInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

The liver is an organ that is particularly exposed to reactive oxygen species (ROS), which not only arise during metabolic functions but also during the biotransformation of xenobiotics. The disruption of redox balance causes oxidative stress, which affects liver function, modulates inflammatory pathways and contributes to disease. Thus, oxidative stress is implicated in acute liver injury and in the pathogenesis of prevalent infectious or metabolic chronic liver diseases such as viral hepatitis B or C, alcoholic fatty liver disease, non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH). Moreover, oxidative stress plays a crucial role in liver disease progression to liver fibrosis, cirrhosis and hepatocellular carcinoma (HCC). Herein, we provide an overview on the effects of oxidative stress on liver pathophysiology and the mechanisms by which oxidative stress promotes liver disease.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.061
GPT teacher head0.352
Teacher spread0.290 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations443
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueAntioxidantsSame topicLiver Disease Diagnosis and TreatmentFrench-language works237,207