Evidence map›Paper›PMID 42642860›Full record

ReviewImmunological reviews2026

The Influenza-Specific Immunoglobulin Repertoire - Nimble Yet Steeped in Tradition.

Sarah F Andrews, Masaru Kanekiyo

Abstract readReview
In one paragraph

Review in Immunological reviews, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Sarah F AndrewsVaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA.ORCID https://orcid.org/0000-0002-2583-1949
Masaru KanekiyoVaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA.ORCID https://orcid.org/0000-0001-5767-1532

Funding

Intramural Research Program of the National Institutes of Health (NIH)
6 · The paper itself

Abstract

Influenza virus has circulated in the human population and co-evolved with us for centuries or longer, shaping our immune repertoires through a continuous host-virus arms race. Across individuals, antibodies repeatedly converge on shared solutions for capturing this shape-shifting virus by precisely targeting conserved sites of vulnerability-sites the virus cannot alter without functional cost-through structural features encoded directly by germline immunoglobulin genes. These recurring public antibodies, termed multi-donor class antibodies, have been pivotal for understanding human anti-influenza immunity and in guiding vaccine strategies that may shift the balance of the host-virus arms race. As single-cell biology, structural biology, and computational approaches mature, previously uncharted areas of our immune repertoires are coming into view. Here, we review the current understanding of the influenza-specific immune repertoire, focusing on antibodies and multi-donor antibody classes directed toward antigenic supersites on viral hemagglutinin and neuraminidase-the two major targets of protective antibodies-and discuss how these insights may advance the development of game-changing influenza vaccines.

Indexed as

Antibodies, ViralInfluenza A virusInfluenza, HumanOrthomyxoviridaeAnimalsAntigens, ViralHemagglutinin Glycoproteins, Influenza VirusHumansInfluenza VaccinesNeuraminidaseAntibodies, ViralAntigens, ViralHemagglutinin Glycoproteins, Influenza VirusInfluenza VaccinesNeuraminidaseantibodieshemagglutinininfluenzamulti‐donor classneuraminidaserepertoirevaccine

Identifiers

PMID42642860
PMCPMC13507380

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.