Influenza vaccines: where we are, where we are going
Bibliographic record
Abstract
Purpose of review Influenza vaccines are the most useful strategy for preventing influenza illness, especially in the setting of the COVID-19 pandemic. For the coming year (2021/2022) all vaccines will be quadrivalent and contain two influenza A strains [(H1N1)pdm09-like and (H3N2)-like viruses] and two influenza B strains (Victoria lineage-like and Yamagata lineage-like viruses). However, the currently licensed have suboptimal efficacy due to the emergence of new strains and vaccine production limitations. In this review, we summarize the current recommendations as well as new advancements in influenza vaccinations. Recent findings Recent advances have been aimed at moving away from egg-based vaccines and toward cell culture and recombinant vaccines. This removes egg adaptations that decrease vaccine efficacy, removes the reliance on egg availability and decreases the time necessary to manufacture vaccines. However, even more radical changes are needed if we are to reach the ultimate goal of a universal vaccine capable of providing long-lasting protection against all or at least most influenza strains. We discuss various strategies, including using more stable influenza antigens such as the hemagglutinin stalk and internal proteins as well as new adjuvants, new vaccine formulations, and DNA/RNA-based vaccines that are currently being developed. Summary The currently available vaccines have suboptimal efficacy and do not provide adequate protection against drifted and shifted strains. Thus, the development of a universal influenza vaccine that induces long-lasing immunity and protects against a broad range of strains is crucial.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".