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Agonistic antibodies to Fas induce a breach in the endothelial lining of the liver and a breakdown in B cell tolerance

2006· article· en· W1571080219 on OpenAlexaff
Marianna M. Newkirk, Urszula Nowak, Emil Skamene, D Iera, Julie Desbarats

Bibliographic record

VenueClinical & Experimental Immunology · 2006
Typearticle
Languageen
FieldMedicine
TopicHepatitis B Virus Studies
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsAntibodyAutoantibodyImmunologyApoptosisFas receptorLiver injuryMiniature swineSplenocyteIsotypeLiver diseaseBiologyMedicineProgrammed cell deathEndocrinologyInternal medicineBiochemistryMonoclonal antibody

Abstract

fetched live from OpenAlex

Liver disease can be associated with a breakdown in self-tolerance and the production of autoantibodies such as rheumatoid factors (RF), which bind to IgG. Here we investigated whether primary, non-infectious liver damage was sufficient to induce autoantibody production. We established a model of targeted liver damage induced by weekly sublethal injections of pro-apoptotic anti-Fas (CD95) antibodies. Liver damage, monitored by measurements of serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels, was minimal 1 week after anti-Fas injection. However, the sublethal Fas stimulation was sufficient to trigger significant haemorrhage in the liver, as assessed by Evans Blue dye leakage into the organ 5 h after anti-Fas antibody injection. We observed an induction of RF in response to the weekly injections of sublethal anti-Fas antibodies but not of isotype control antibodies, indicating a breakdown of self-tolerance induced by Fas engagement. RF induction was unlikely to be due to direct activation of B cells, as splenocytes stimulated with anti-Fas antibodies in vitro did not produce RF. These studies show that sublethal damage to the liver by Fas engagement leads to liver haemorrhage and is sufficient to trigger the breakdown of self-tolerance.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.914

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 integrity0.0000.000
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 head0.038
GPT teacher head0.354
Teacher spread0.316 · 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

Citations1
Published2006
Admission routes1
Has abstractyes

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