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Rheumatoid factor induction in murine models of liver injury

2006· article· en· W2023784867 on OpenAlexaff
Urszula Nowak, Kathryn Gill, Emil Skamene, Marianna M. Newkirk

Bibliographic record

VenueClinical & Experimental Immunology · 2006
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsLiver injuryRheumatoid factorRheumatoid arthritisMedicineLiver diseaseImmunologyAlcoholic liver diseaseAutoantibodyCarbon tetrachlorideChronic liver diseaseInternal medicineEndocrinologyChemistryCirrhosisAntibody

Abstract

fetched live from OpenAlex

Alcoholic liver disease and hepatitis C are associated with the production of autoantibodies such as rheumatoid factors (RF), which bind to IgG and can aid in host defence, but are also associated with pathological conditions such as rheumatoid arthritis. Because little is known about the role of RF in liver disease, we characterized the RF production that either occurred spontaneously in response to alcohol consumption or was induced by injection of an Escherichia coli glycolipoprotein in C57Bl/6 mice. Whereas severe liver damage was induced by carbon tetrachloride (CCl(4)), minimal damage was caused by chronic alcohol consumption. Liver damage was monitored by measurements of serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST). Circulating RF was induced in response to chronic alcohol consumption; the latter probably involved Toll-like receptor ligation. In contrast, CCl(4)-induced damage was not associated with RF induction. However, concurrent treatment with an E. coli glycolipoprotein macromolecule that induced RF, protected against CCL(4)-induced liver damage as measured by a highly significant decrease (P = 0.008) at 4 weeks in AST and ALT. RF induced by E. coli glycolipoprotein correlated with 'protection' from liver damage, indicating that the RF autoimmune response does not necessarily exacerbate 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 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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.826

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.101
GPT teacher head0.427
Teacher spread0.326 · 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 designBench or experimental
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

Citations11
Published2006
Admission routes1
Has abstractyes

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