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Record W2272177911 · doi:10.1098/rsif.2015.0627

A simple mechanistic explanation for original antigenic sin and its alleviation by adjuvants

2015· article· en· W2272177911 on OpenAlexfundno aff
Wilfred Ndifon

Bibliographic record

VenueJournal of The Royal Society Interface · 2015
Typearticle
Languageen
FieldImmunology and Microbiology
TopicT-cell and B-cell Immunology
Canadian institutionsnot available
FundersNational Eye InstituteInternational Development Research Centre
KeywordsAntigenAcquired immune systemImmunityMechanism (biology)ImmunologyImmune systemBiologyPhysics

Abstract

fetched live from OpenAlex

A large number of published studies have shown that adaptive immunity to a particular antigen, including pathogen-derived, can be boosted by another, cross-reacting antigen while inducing suboptimal immunity to the latter. Although this phenomenon, called original antigenic sin (OAS), was first reported approximately 70 years ago (Francis et al. 1947 Am. J. Public Health 37, 1013-1016 (doi:10.2105/AJPH.37.8.1013)), its underlying biological mechanisms are still inadequately understood (Kim et al. Proc. Natl Acad. Sci. USA 109, 13 751-13 756 (doi:10.1073/pnas.0912458109)). Here, focusing on the humoral aspects of adaptive immunity, I propose a simple and testable mechanism: that OAS occurs when T regulatory cells induced by the first antigen decrease the dose of the second antigen that is loaded by dendritic cells and available to activate naive lymphocytes. I use both a parsimonious mathematical model and experimental data to confirm the deductive validity of this proposal. This model also explains the puzzling experimental observation that administering certain dendritic cell-activating adjuvants during antigen exposure alleviates OAS. Specifically, the model predicts that such adjuvants will attenuate T regulatory suppression of naive lymphocyte activation. Together, these results suggest additional strategies for redeeming adaptive immunity from the destructive consequences of antigenic 'sin'.

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 score0.049
Threshold uncertainty score0.319

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 studies0.0000.000
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 head0.020
GPT teacher head0.263
Teacher spread0.243 · 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

Citations30
Published2015
Admission routes1
Has abstractyes

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