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Increasing the foreignness of an antigen, by coupling a second and foreign antigen to it, increases the T helper type 2 component of the immune response to the first antigen

2005· article· en· W2021994270 on OpenAlexaff
Nahed Ismail, Antony Basten, Helen Briscoe, Peter A. Bretscher

Bibliographic record

VenueImmunology · 2005
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAntigenComponent (thermodynamics)Immune systemImmunologyBiologyPhysics

Abstract

fetched live from OpenAlex

It has been proposed that the degree of an antigen's foreignness is important in determining the Th1/Th2 phenotype of the immune response it generates. We test this hypothesis here and partially dissect the underlying mechanism. Immunization of C57BL/6 and hen egg lysozyme (HEL)-transgenic mice, tolerant to HEL at the T-cell level, with low doses of sheep red blood cells (SRBC), generated a predominant T helper type 1 (Th1) response in both mouse strains. However, substantial numbers of SRBC-specific Th2 cells were generated when normal, but not HEL-transgenic, mice were immunized with a low dose of the conjugate HEL-SRBC. The generation of these anti-SRBC Th2 cells in normal mice required that HEL be coupled to SRBC, since HEL was ineffective in deviating the response to SRBC when present but coupled to another, non-cross-reacting, xenogeneic RBC. This Th2 deviation of the anti-SRBC response by HEL thus requires the operational recognition of HEL epitopes linked to SRBC. Thus increasing the foreignness of an antigen increases its ability to generate Th2 cells. Our findings, in the context of previous observations in related systems, support the proposal that more CD4(+) T-cell/CD4(+) T-cell interactions, mediated by the operational recognition of linked epitopes, are required to generate Th2 cells than Th1 cells.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.532
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.010
GPT teacher head0.239
Teacher spread0.229 · 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

Citations22
Published2005
Admission routes1
Has abstractyes

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