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CD47 deficiency ameliorates autoimmune nephritis in Faslpr mice via suppressing IgG autoantibody production (THER5P.912)

2015· article· en· W4313385868 on OpenAlexaff
Lei Shi, Zhen Bian, Zhiyuan Lv, Yuan Liu

Bibliographic record

VenueThe Journal of Immunology · 2015
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPhagocytosis and Immune Regulation
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsAutoantibodyLupus nephritisImmunologyGerminal centerSystemic lupus erythematosusNephritisAntibodyMedicineB cellInternal medicineDisease

Abstract

fetched live from OpenAlex

Abstract CD47, a self-recognition marker, significantly regulates both innate and adaptive immune response. To explore the potential role of CD47 in activation of autoreactive T and B cells and the production of autoantibodies in diseases such as systemic lupus erythematosus, we generated CD47-knockout in spontaneous lupus mice (Faslpr). In comparison with Faslpr mice, CD47-/--Faslpr mice exhibited a prolonged lifespan, with no apparent signs of autoimmune nephritis including glomerular cell proliferation, acute tubular atrophy and vacuolization. CD47-/--Faslpr mice had lower level of proteinuria, lower deposition of C3 and C1q in the glomeruli and lesser pronounced splenomegaly compared to the age-matched Faslpr mice. Serum levels of antinuclear antibodies and anti-double-stranded DNA antibodies in CD47-/--Faslpr mice were also significantly reduced. The mechanistic studies further suggested that CD47 deficiency might impair the antigenic challenge-induced production of mouse high affinity IgG but not IgM through reducing T follicular cell production, which results in an impaired formation of germinal centers in lymph tissue. In conclusion, our results demonstrate for the first time that CD47 deficiency ameliorates lupus nephritis in Faslpr mice via suppressing IgG autoantibody production.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.019
GPT teacher head0.254
Teacher spread0.235 · 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 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

Citations1
Published2015
Admission routes1
Has abstractyes

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