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Record W4392095492 · doi:10.1016/j.xcrm.2024.101433

Adjuvanted nanoliposomes displaying six hemagglutinins and neuraminidases as an influenza virus vaccine

2024· article· en· W4392095492 on OpenAlexafffund
Zachary R. Sia, Jayishnu Roy, Wei‐Chiao Huang, Yiting Song, Shiqi Zhou, Yuan Luo, Qinzhe Li, Dominic Arpin, Hilliard L. Kutscher, Joaquı́n Ortega, Bruce A. Davidson, Jonathan F. Lovell

Bibliographic record

VenueCell Reports Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsMcGill University
FundersVA Western New York Healthcare SystemUniversity at BuffaloNational Institutes of HealthMcGill UniversityCenters for Disease Control and PreventionU.S. Department of Veterans Affairs
KeywordsVirologyNeuraminidaseHemagglutinin (influenza)VirusVaccinationBiologyInfluenza vaccineRecombinant DNAAntibodyAntigenMicrobiologyImmunology

Abstract

fetched live from OpenAlex

Inclusion of defined quantities of the two major surface proteins of influenza virus, hemagglutinin (HA) and neuraminidase (NA), could benefit seasonal influenza vaccines. Recombinant HA and NA multimeric proteins derived from three influenza serotypes, H1N1, H3N2, and type B, are surface displayed on nanoliposomes co-loaded with immunostimulatory adjuvants, generating "hexaplex" particles that are used to immunize mice. Protective immune responses to hexaplex liposomes involve functional antibody elicitation against each included antigen, comparable to vaccination with monovalent antigen particles. When compared to contemporary recombinant or adjuvanted influenza virus vaccines, hexaplex liposomes perform favorably in many areas, including antibody production, T cell activation, protection from lethal virus challenge, and protection following passive sera transfer. Based on these results, hexaplex liposomes warrant further investigation as an adjuvanted recombinant influenza vaccine formulation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.662
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.052
GPT teacher head0.385
Teacher spread0.333 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations17
Published2024
Admission routes2
Has abstractyes

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