MétaCan
Menu
← Back to cohort

Antigen-specific precursor frequencies influence requirement of CD4+ T cell help and CD40-40L costimulation in primary and secondary responses (132.8)

2010· article· en· W2292443943 on OpenAlexaff
Channakeshava Sokke Umeshappa, Jim Xiang

Bibliographic record

VenueThe Journal of Immunology · 2010
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsSaskatchewan Cancer AgencyUniversity of Saskatchewan
Fundersnot available
KeywordsPriming (agriculture)CD8ImmunologyCytotoxic T cellT cellCD40AntigenBiologyImmune systemGenetics

Abstract

fetched live from OpenAlex

Abstract CD4+ T cells play a decisive role in generation of effective CD8+ T cell-mediated immunity against diseases associated with minor H antigens, including cancer, auto-immunity and allogenic tissue transplantation. The increased precursor frequency in reducing the need of CD4+ T cell help and costimulations in CD8+ T cell primary responses have been demonstrated. However, its similar roles in memory CD8+ T cell responses have not been fully understood. In a non-infectious vaccine model, here we show that, at endogenous precursor levels, CD4+ T cell help, and CD40-40L costimulation are required for both primary and memory responses. At increased precursor frequency, although CD4+ T cell help, and CD40-40L costimulation are not obligatory in primary responses, their requirement is still necessary for functional memory responses. Despite increased precursor frequency is ensured during priming, the memory CD8+ T cells developed from increased precursor frequency in the absence of CD4+ T cell help, and CD40-40L costimulation fail to induce complete protection following tumor challenge. These results further support the previous observations that efficient memory CD8+ T cell responses require CD4+ T cell help and CD40-40L costimulation.

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)0.001
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.236
Teacher spread0.224 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Immunology→Same topicImmunotherapy and Immune Responses→French-language works237,207→