Intranasal hemagglutinin protein boosters induce robust mucosal immunity and cross-protection against influenza A viral challenge
Bibliographic record
Abstract
Abstract Licensed parenteral influenza vaccines induce systemic antibody responses and alleviate disease severity but do not efficiently prevent viral entry and transmission due to the lack of local mucosal immune responses. Here, we describe intranasal booster strategy with unadjuvanted recombinant hemagglutinin (HA) following initial mRNA-LNP vaccination, Prime and HA. This regimen establishes highly protective HA-specific mucosal immune memory responses in the respiratory tract. Intranasal HA boosters provided significantly reduced viral replication compared to parenteral mRNA-LNP boosters in both young and old mice. Correlation analysis revealed that slightly increased levels of nasal IgA are significantly associated with a reduced viral burden in the upper respiratory tract. Intranasal boosting with an antigenically distinct H1 HA conferred sterilizing immunity against heterologous H1N1 virus challenge. Additionally, a heterosubtypic intranasal H5 HA booster elicited cross-reactive mucosal humoral responses. Our work illustrates the potential of a nasal HA protein booster as a needle- and adjuvant-free strategy to prevent infection and disease from influenza A viruses. One Sentence Summary Adjuvant-free nasal booster induces protective immunity against influenza infection.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".