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Record W2137966793 · doi:10.4049/jimmunol.167.10.5669

Immunological Memory: Contribution of Memory B Cells Expressing Costimulatory Molecules in the Resting State

2001· article· en· W2137966793 on OpenAlexafffund
Amit Bar‐Or, Enedina Maria Lobato de Oliveira, David E. Anderson, Jeff I. Krieger, Martin Duddy, Kevin C. O’Connor, David A. Hafler

Bibliographic record

VenueThe Journal of Immunology · 2001
Typearticle
Languageen
FieldImmunology and Microbiology
TopicT-cell and B-cell Immunology
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersNational Institute of Neurological Disorders and StrokeNational Institute of Diabetes and Digestive and Kidney DiseasesU.S. Public Health ServiceMultiple Sclerosis SocietyMultiple Sclerosis Society of CanadaMassachusetts Institute of TechnologyNational Institutes of HealthPfizer
KeywordsImmunological memoryState (computer science)Resting state fMRIBiologyNeuroscienceImmunologyImmune systemComputer scienceImmunity

Abstract

fetched live from OpenAlex

Traditionally, emphasis has been placed on the roles of Th cells in generating and amplifying both cellular and humoral memory responses. Little is known about the potential contributions of B cell subsets to immunological memory. Resting memory B cells have generally been regarded as poor APC, attributed in part to the relative paucity of costimulatory molecules identified on their surface. We describe a novel subpopulation of human memory B cells that express CD80 in their resting state, are poised to secrete particularly large amounts of class switched Igs, and can efficiently present Ag to and activate T cells. This functionally distinct B cell subset may represent an important mechanism by which quiescent human B cells can initiate and propagate rapid and vigorous immune memory responses. Finally, these studies extend recent observations in the murine system and highlight the phenotypic and functional diversity that exists within the human B cell memory compartment.

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)0.003
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 score0.040
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.016
GPT teacher head0.242
Teacher spread0.226 · 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

Citations139
Published2001
Admission routes2
Has abstractyes

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