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Record W2885579062 · doi:10.1111/imcb.12197

Constitutive overexpression of TNF in BPSM1 mice causes iBALT and bone marrow nodular lymphocytic hyperplasia

2018· article· en· W2885579062 on OpenAlexfundno aff
Cyril Seillet, Elyas H. Arvell, Derek Lacey, Michael D. Stutz, Marc Pellegrini, Lachlan Whitehead, Joel S. Rimes, Edwin D. Hawkins, Ben Roediger, Gabrielle T. Belz, Philippe Bouillet

Bibliographic record

VenueImmunology and Cell Biology · 2018
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsnot available
FundersStanford Institute for Research in the Social SciencesInstitute of Medical Science, University of TokyoGenentechCampbell Family Institute for Breast Cancer ResearchSylvia and Charles Viertel Charitable FoundationNational Health and Medical Research CouncilArthritis AustraliaLeukemia and Lymphoma Society
KeywordsBone marrowTumor necrosis factor alphaPolyarthritisHyperplasiaPathologyCytokineBiologyMedicineImmunologyArthritis

Abstract

fetched live from OpenAlex

BPSM1 (Bone phenotype spontaneous mutant 1) mice develop severe polyarthritis and heart valve disease as a result of a spontaneous mutation in the Tnf gene. In these mice, the insertion of a retrotransposon in the 3' untranslated region of Tnf causes a large increase in the expression of the cytokine. We have found that these mice also develop inducible bronchus-associated lymphoid tissue (iBALT), as well as nodular lymphoid hyperplasia (NLH) in the bone marrow. Loss of TNFR1 prevents the development of both types of follicles, but deficiency of TNFR1 in the hematopoietic compartment only prevents the iBALT and not the NLH phenotype. We show that the development of arthritis and heart valve disease does not depend on the presence of the tertiary lymphoid tissues. Interestingly, while loss of IL-17 or IL-23 limits iBALT and NLH development to some extent, it has no effect on polyarthritis or heart valve disease in BPSM1 mice.

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 score0.185
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.006
GPT teacher head0.238
Teacher spread0.231 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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