“Gnothi Seauton”: Leveraging the Host Response to Improve Influenza Virus Vaccine Efficacy
Bibliographic record
Abstract
Vaccination against the seasonal influenza virus is the best way to prevent infection. Nevertheless, vaccine efficacy remains far from optimal especially in high-risk populations such as the elderly. Recent technological advancements have facilitated rapid and precise identification of the B and T cell epitopes that are targets for protective responses. While these discoveries have undoubtedly brought the field closer to "universal" influenza virus vaccines, choosing the correct antigen is only one piece of the equation. Achieving efficacy and durability requires a detailed understanding of the diverse host factors and pathways that are required for attaining optimal responses. Sequencing technologies, systems biology, and immunological studies have recently advanced our understanding of the diverse aspects of the host response required for vaccine efficacy. In this paper, we review the critical role of the host response in determining efficacious responses and discuss the gaps in knowledge that will need to be addressed if the field is to be successful in developing new and more effective influenza virus vaccines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".