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Record W4292182123 · doi:10.1016/j.xcrm.2022.100718

Hemagglutinin stalk-binding antibodies enhance effectiveness of neuraminidase inhibitors against influenza via Fc-dependent effector functions

2022· article· en· W4292182123 on OpenAlexafffund
Ali Zhang, Hanu Chaudhari, Yonathan Agung, Michael R. D’Agostino, Jann C. Ang, Yona Tugg, Matthew S. Miller

Bibliographic record

VenueCell Reports Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health ResearchMichael G. DeGroote Institute for Infectious Disease Research, McMaster UniversityBoler Family Foundation
KeywordsNeuraminidaseHemagglutinin (influenza)EffectorVirologyAntibodyNeuraminidase inhibitorInfluenza A virusBiologyChemistryMicrobiologyImmunologyVirusMedicineCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

The conserved hemagglutinin stalk domain is an attractive target for broadly effective antibody-based therapeutics and next-generation universal influenza vaccines. Protection provided by hemagglutinin stalk-binding antibodies is principally mediated through activation of immune effector cells. Titers of stalk-binding antibodies are highly variable on an individual level and tend to increase with age as a result of increasing exposures to influenza virus. In our study, we show that stalk-binding antibodies cooperate with neuraminidase inhibitors to protect against influenza virus infection in an Fc-dependent manner. These data suggest that the effectiveness of neuraminidase inhibitors is likely influenced by an individual's titers of stalk-binding antibodies and that neuraminidase inhibitors may enhance the effectiveness of future stalk-binding monoclonal antibody-based treatments.

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)0.004
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.031
GPT teacher head0.341
Teacher spread0.310 · 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

Citations15
Published2022
Admission routes2
Has abstractyes

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