Evidence map›Paper›PMID 28724768›Full record

ArticleJournal of virology2017

Synthetic Toll-Like Receptor 4 (TLR4) and TLR7 Ligands Work Additively via MyD88 To Induce Protective Antiviral Immunity in Mice.

Peter H Goff, Tomoko Hayashi, Wenqian He, Shiyin Yao, Howard B Cottam, Gene S Tan, Brian Crain, Florian Krammer, Karen Messer, Minya Pu and 3 more

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of virology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed, 35 citations in OpenAlex.

  1. Pangenome-GuidedPharmaceuticals (Basel, Switzerland) · 2026
    Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

13 authors at 2 institutions in 1 country.

Peter H GoffDepartment of Microbiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.ORCID http://orcid.org/0000-0002-3488-4997
Tomoko HayashiMoores UCSD Cancer Center, University of California San Diego, La Jolla, California, USA.
Wenqian HeDepartment of Microbiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Shiyin YaoMoores UCSD Cancer Center, University of California San Diego, La Jolla, California, USA.
Howard B CottamMoores UCSD Cancer Center, University of California San Diego, La Jolla, California, USA.
Gene S TanDepartment of Microbiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Brian CrainMoores UCSD Cancer Center, University of California San Diego, La Jolla, California, USA.
Florian KrammerDepartment of Microbiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.ORCID http://orcid.org/0000-0003-4121-776X
Karen MesserMoores UCSD Cancer Center, University of California San Diego, La Jolla, California, USA.
Minya PuMoores UCSD Cancer Center, University of California San Diego, La Jolla, California, USA.
Dennis A CarsonMoores UCSD Cancer Center, University of California San Diego, La Jolla, California, USA.
Peter PaleseDepartment of Microbiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA peter.palese@mssm.edu mpcorr@ucsd.edu.
Maripat CorrDepartment of Medicine, University of California San Diego, La Jolla, California, USA peter.palese@mssm.edu mpcorr@ucsd.edu.
University of California San Diego · USIcahn School of Medicine at Mount Sinai · US

Funding

VIRAL MALIGNANCYP30CA023100 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI DIANE M SIMEONE · 1985 to 2026
$124.9M
MOUNT SINAI MEDICAL SCIENTIST TRAINING PROGRAMT32GM007280 · NIGMS · MOUNT SINAI SCHOOL OF MEDICINE OF NYU · PI BARON, MARGARET H · 1985 to 2021
$22.8M
NCI NIH HHS P30 CA023100NIAID NIH HHS HHSN272200900032CNIAID NIH HHS HHSN272200900034CNIAID NIH HHS HHSN272201400008CNIAID NIH HHS HHSN272201400051CNIGMS NIH HHS T32 GM007280
6 · The paper itself

Abstract

We previously demonstrated that the combination of synthetic small-molecule Toll-like receptor 4 (TLR4) and TLR7 ligands is a potent adjuvant for recombinant influenza virus hemagglutinin, inducing rapid and sustained immunity that is protective against influenza viruses in homologous, heterologous, and heterosubtypic murine challenge models. Combining the TLR4 and TLR7 ligands balances Th1 and Th2-type immune responses for long-lived cellular and neutralizing humoral immunity against the viral hemagglutinin. Here, we demonstrate that the protective response induced in mice by this combined adjuvant is dependent upon TLR4 and TLR7 signaling via myeloid differentiation primary response gene 88 (MyD88), indicating that the adjuvants function

Indexed as

Adaptor Proteins, Vesicular TransportAdjuvants, ImmunologicAnimalsAntibodies, ViralAntigens, ViralFemaleHemagglutinin Glycoproteins, Influenza VirusInfluenza A Virus, H1N1 SubtypeInfluenza VaccinesLungMembrane GlycoproteinsMiceMice, Inbred BALB CMice, Inbred C57BLMice, KnockoutMyeloid Differentiation Factor 88Adaptor Proteins, Vesicular TransportAdjuvants, ImmunologicAntibodies, ViralAntigens, ViralHemagglutinin Glycoproteins, Influenza VirusInfluenza VaccinesMembrane GlycoproteinsMyd88 protein, mouseMyeloid Differentiation Factor 88Ticam1 protein, mouseTlr4 protein, mouseTlr7 protein, mouseToll-Like Receptor 4Toll-Like Receptor 7adjuvantinfluenza virusTLR4TLR7Toll-like receptorvaccine

Identifiers

PMID28724768
PMCPMC5599744
OpenAlexW2739154332

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.