Evidence map›Paper›PMID 41971165›Full record

ArticleBio-protocol2026

Preparing Adjuvanted Nanoliposomes for Applications Toward Recombinant Influenza Vaccine Development.

Zachary R Sia, Wei-Chiao Huang, Matthew Willadsen, Hilliard L Kutscher, Jonathan F Lovell, Bruce A Davidson

Abstract read
In one paragraph

Article in Bio-protocol, 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

6 authors.

Zachary R SiaDeparment of Anesthesiology, Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, USA.
Wei-Chiao HuangPOP Biotechnologies, Buffalo, NY, USA.
Matthew WilladsenPOP Biotechnologies, Buffalo, NY, USA.
Hilliard L KutscherPOP Biotechnologies, Buffalo, NY, USA.
Jonathan F LovellDepartment of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, NY, USA.
Bruce A DavidsonDeparment of Anesthesiology, Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nanoparticle vaccines can provide advantages over traditional vaccine methodologies, including adjuvant delivery to enhance the effectiveness of recombinant antigens. Many approaches exist to formulate different vaccine nanoparticles, which are designed for different biomolecular cargos, adjuvant compositions, and disease targets. Here, a protocol is described to produce nanoliposomes whose surface is decorated with recombinant protein influenza antigens with monophosphoryl lipid A and QS-21 adjuvants incorporated into the lipid bilayer for protection against influenza infection. This protocol includes methods for producing adjuvanted liposomes and coupling with His-tagged antigens for surface decoration of the particle. This allows for a rapid methodology of producing immunogenic antigen-presenting liposomes that can be tailored to display a combination of influenza surface antigens. Key features • This protocol uses recombinant proteins, which can be adapted to the desired strain sequence. • Production of this influenza vaccine formulation uses in vitro techniques, eliminating the need for virus growth in eggs. • Additional lipid adjuvants can be included in the liposome production step to be incorporated into the surface membrane. • Cobalt-porphyrin phospholipid provides a flexible platform for binding His-tag-bearing proteins.

Indexed as

InfluenzaNanoliposomesNanoparticle vaccineRecombinant protein vaccineVaccine adjuvant

Identifiers

PMID41971165
PMCPMC13067150

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.