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Record W4413140931 · doi:10.1016/j.vaccine.2025.127393

The impact of egg adaptation and immune imprinting on influenza vaccine effectiveness

2025· article· en· W4413140931 on OpenAlexaff
Mansoor Ashraf, Alicia Stein, John Youhanna, Steven Rockman, Meagan McMahon, Ian McGovern, Sankarasubramanian Rajaram, Matthew S. Miller

Bibliographic record

VenueVaccine · 2025
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsMcMaster University
FundersSeqirus
KeywordsBiologyImmune systemImprinting (psychology)VaccinationAntigenImmunologyVirologyInfluenza vaccineOriginal antigenic sinAntibodyVirusAntigenic driftInfluenza A virusGenetics

Abstract

fetched live from OpenAlex

Most influenza vaccines are produced in hens' eggs and may undergo ‘egg adaptation’, whereby mutations within the haemagglutinin protein that result in adaptation to the avian cells undergo positive selection. It is well established that egg adaptation can impact antigenicity and vaccine effectiveness (VE) by causing mismatches between the vaccine virus and circulating viruses. However, few studies have investigated the potentially long-lasting impact of childhood vaccination with an egg-adapted vaccine on the immunological memory. Prior exposure history shapes subsequent immune responses, such that memory responses to previously encountered antigens trigger stronger immune responses than those elicited by de novo antigen exposure. This phenomenon is called immune imprinting, when referring specifically to the impact of the first lifetime exposure, and antigenic seniority, when referring to exposures after the first, which also shape an individual's antibody repertoire according to how early and how often they are encountered. Crucially, if an individual's first influenza exposure is via an egg-adapted vaccine, this imprinting event could adversely affect antibody responses to circulating viruses in future seasons, reducing the benefit of influenza vaccination. Using alternative types of vaccines that avoid egg adaptation is particularly important now that the World Health Organization (WHO) recommend immunising children aged ≥6 months against influenza. In this review, we cover the historical frequency and nature of egg adaptations and the impact of egg adaptation, immune imprinting and antigenic seniority on the clinical effectiveness of seasonal influenza vaccinations. We discuss the impact of interactions between egg adaptation and immune imprinting, examine how egg-adapted vaccines can lead to suboptimal imprinting and potentially reduce VE throughout an individual's lifetime, and identify how we can address this issue in future. • A person's first exposure to influenza has a life-long impact on immune response. • To improve effectiveness, vaccine viruses should closely match circulating strains. • Egg adaptations can alter the antigenicity of the vaccines and reduce effectiveness. • Egg adaptations have been identified in most egg-based influenza vaccine viruses. • Childhood immunisation with non-egg-based vaccines could optimise immune imprinting.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.398
Teacher spread0.355 · 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 designObservational
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

Citations11
Published2025
Admission routes1
Has abstractyes

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