MétaCan
Menu
Back to cohort

Long‐Term Binge and Escalating Ethanol Exposure Causes Necroinflammation and Fibrosis in Rat Liver

2012· article· en· W2076170238 on OpenAlexaff
Junying Zhou, Zhian Jiang, Zhen Zhen, Wei Wang, Amin A. Nanji

Bibliographic record

VenueAlcoholism Clinical and Experimental Research · 2012
Typearticle
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsDalhousie University
Fundersnot available
KeywordsProinflammatory cytokineOxidative stressSteatosisEndocrinologyInternal medicineMedicineTumor necrosis factor alphaFibrosisAlcoholic liver diseaseInflammationPathogenesisCyclooxygenaseChemistryCirrhosisBiochemistry

Abstract

fetched live from OpenAlex

BACKGROUND: To investigate whether "binge" and escalating alcohol exposure in the rat influences the development of pathological liver injury. METHODS: Time courses for the formation of eicosanoids by cyclooxygenase (COX), oxidative stress and nitrosative stress production, expression of hypoxia-inducible factor 1 (HIF-1), cytokines, hepatic tissue necroinflammation, and fibrosis were assessed in rats during 16 weeks of daily alcohol gavage. RESULTS: In this model of binge and escalating levels of alcohol, hepatic steatosis, necrosis, and inflammation as well as fibrosis were increased over the 16-week period. The levels of COX-2, oxidative stress, nitrosative stress, HIF-1, proinflammatory mediators (tumor necrosis factor-α, interleukin 1(β) [IL-1(β) ], IL-6), and procollagen-I were increased over the 16-week period. The content of IL-10 in rat serum increased at the end of 4 and 8 weeks but decreased thereafter and was significantly decreased at 12 and 16 weeks. CONCLUSIONS: A rat model of alcoholic liver disease (ALD) with long-term binge and escalating ethanol exposure was developed. Our data support the hypothesis that enhanced eicosanoid production by COX, oxidative stress and nitrosative stress, HIF-1, and the imbalance between pro- and anti-inflammatory cytokines plays an important role in the pathogenesis of ALD.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.326
GPT teacher head0.524
Teacher spread0.199 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations26
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueAlcoholism Clinical and Experimental ResearchSame topicAlcohol Consumption and Health EffectsFrench-language works237,207