ArticleScientific reports2019
Predicting Antigenicity of Influenza A Viruses Using biophysical ideas.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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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.
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
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- Sequence-based detection of emerging antigenically novel influenza A viruses.Proceedings. Biological sciences · 2024Article
- End-to-end antigenic variant generation for H1N1 influenza HA protein using sequence to sequence models.PloS one · 2022Article
- Article
- CAvant® WO-60 as an Effective Immunological Adjuvant for Avian Influenza and Newcastle Disease Vaccine.Frontiers in veterinary science · 2021Article
- A Herbal Mixture Formula of OCD20015-V009 Prophylactic Administration to Enhance Interferon-Mediated Antiviral Activity Against Influenza A Virus.Frontiers in pharmacology · 2021Article
- Influenza Anti-Stalk Antibodies: Development of a New Method for the Evaluation of the Immune Responses to Universal Vaccine.Vaccines · 2020Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antigenic variations of influenza A viruses are induced by genomic mutation in their trans-membrane protein HA1, eliciting viral escape from neutralization by antibodies generated in prior infections or vaccinations. Prediction of antigenic relationships among influenza viruses is useful for designing (or updating the existing) influenza vaccines, provides important insights into the evolutionary mechanisms underpinning viral antigenic variations, and helps to understand viral epidemiology. In this study, we present a simple and physically interpretable model that can predict antigenic relationships among influenza A viruses, based on biophysical ideas, using both genomic amino acid sequences and experimental antigenic data. We demonstrate the applicability of the model using a benchmark dataset of four subtypes of influenza A (H1N1, H3N2, H5N1, and H9N2) viruses and report on its performance profiles. Additionally, analysis of the model's parameters confirms several observations that are consistent with the findings of other previous studies, for which we provide plausible explanations.
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Registered trials
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 Socratic graph.