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Record W4311668559 · doi:10.1186/s40001-022-00933-3

Splenectomy induces biochemical remission and regeneration in experimental murine autoimmune hepatitis

2022· article· en· W4311668559 on OpenAlexafffund
Janine Dywicki, Laura Elisa Buitrago‐Molina, Fatih Noyan, Jérôme Schlué, Konstantinos Iordanidis, Michael P. Manns, Heiner Wedemeyer, Elmar Jaeckel, Matthias Hardtke‐Wolenski

Bibliographic record

VenueEuropean journal of medical research · 2022
Typearticle
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersDeutsche ForschungsgemeinschaftEuropean CommissionGovernment of Canada
KeywordsSplenectomyImmunologyCD8Autoimmune hepatitisSpleenMedicineImmune systemLiver regenerationRegeneration (biology)Liver diseasePathophysiologyLiver transplantationHepatitisTransplantationInternal medicineBiology

Abstract

fetched live from OpenAlex

Abstract Autoimmune hepatitis (AIH) is a chronic immune-mediated inflammatory liver disease. It is known that AIH originates not from the spleen but from the liver itself. Nonetheless, most details of the etiology and pathophysiology are unknown. We induced experimental murine AIH (emAIH) in NOD/Ltj mice by single administration of a replication-deficient adenovirus and performed splenectomy during late-stage disease. Biochemical disease remission occurred, which was characterized by improvement in transaminase levels. The causes of this remission included a shift in the transcriptomic signature of serum proteins toward regeneration. At the cellular level, there was a marked decrease in activated CD8 + T cells and an increase in intrahepatic regulatory T cells (Tregs). Here, intrahepatic Treg numbers correlated with biochemical remission. Notably, an imbalance in the T-cell/B-cell ratio was observed, with a disproportionate increase in total B cells. In summary, intrahepatic increases in Tregs, biochemical remission, and regeneration could be induced by splenectomy in the late stage of emAIH.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.733
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.070
GPT teacher head0.385
Teacher spread0.315 · 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.

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

Citations0
Published2022
Admission routes2
Has abstractyes

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