Evidence map›Paper›PMID 33440898›Full record

ArticleVaccines2021

Chimeric Hemagglutinin-Based Live-Attenuated Vaccines Confer Durable Protective Immunity against Influenza A Viruses in a Preclinical Ferret Model.

Wen-Chun Liu, Raffael Nachbagauer, Daniel Stadlbauer, Shirin Strohmeier, Alicia Solórzano, Francesco Berlanda-Scorza, Bruce L Innis, Adolfo García-Sastre, Peter Palese, Florian Krammer and 1 more

Open access · goldAbstract read
In one paragraph

Article in Vaccines, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
2.9field-weighted citation impact, top 9% of its field
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

17 citing papers in PubMed, 24 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Review
  14. Article
  15. Review
  16. Review
  17. Review
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

11 authors at 5 institutions in 2 countries.

Wen-Chun LiuDepartment of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Raffael NachbagauerDepartment of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Daniel StadlbauerDepartment of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Shirin StrohmeierDepartment of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Alicia SolórzanoDepartment of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Francesco Berlanda-ScorzaPATH US, Seattle, WA 98121, USA.
Bruce L InnisBiomedical Translation Research Center, Academia Sinica, Taipei 11571, Taiwan.
Adolfo García-SastreDepartment of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.ORCID 0000-0002-6551-1827
Peter PaleseDepartment of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.ORCID 0000-0002-0337-5823
Florian KrammerDepartment of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.ORCID 0000-0003-4121-776X
Randy A AlbrechtDepartment of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.ORCID 0000-0003-4008-503X
Icahn School of Medicine at Mount Sinai · USInstitute of Biomedical Sciences, Academia Sinica · TWModerna Therapeutics (United States) · USProgram for Appropriate Technology in Health · USTisch Hospital · US

Funding

Toward a Universal Influenza Virus VaccineP01AI097092 · NIAID · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI PALESE, PETER · 2012 to 2022
$17.6M
Understanding Human B Cell Responses to Influenza to Improve the Influenza U19AI109946 · NIAID · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI PALESE, PETER · 2014 to 2018
$9.6M
Developing immune reagents to increase the preclinical value of the ferret modelR03AI142046 · NIAID · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI ALBRECHT, RANDY A. · 2019 to 2020
$170k
Bill and Melinda Gates Foundation OPP1084518Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases U19 AI109946, P01 AI097092, R03 AI142046-02, and HHSN272201400008CNIAID NIH HHS P01 AI097092NIAID NIH HHS R03 AI142046PATH GAT.1878-01162340-COL
6 · The paper itself

Abstract

Epidemic or pandemic influenza can annually cause significant morbidity and mortality in humans. We developed novel chimeric hemagglutinin (cHA)-based universal influenza virus vaccines, which contain a conserved HA stalk domain from a 2009 pandemic H1N1 (pH1N1) strain combined with globular head domains from avian influenza A viruses. Our previous reports demonstrated that prime-boost sequential immunizations induced robust antibody responses directed toward the conserved HA stalk domain in ferrets. Herein, we further followed vaccinated animals for one year to compare the efficacy and durability of these vaccines in the preclinical ferret model of influenza. Although all cHA-based immunization regimens induced durable HA stalk-specific and heterosubtypic antibody responses in ferrets, sequential immunization with live-attenuated influenza virus vaccines (LAIV-LAIV) conferred the best protection against upper respiratory tract infection by a pH1N1 influenza A virus. The findings from this study suggest that our sequential immunization strategy for a cHA-based universal influenza virus vaccine provides durable protective humoral and cellular immunity against influenza virus infection.

Indexed as

chimeric hemagglutininferrethemagglutinin stalk antibodylong-lived immunityuniversal influenza virus vaccine

Identifiers

PMID33440898
PMCPMC7826668
OpenAlexW3119127733

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.