Intranasal hemagglutinin protein boosters induce protective mucosal immunity against influenza A viruses in mice
Bibliographic record
Abstract
Licensed parenteral influenza vaccines induce systemic antibody responses and alleviate disease severity but do not efficiently induce local mucosal immune responses. Here, we describe an intranasal booster strategy with unadjuvanted recombinant hemagglutinin (HA) following initial messenger RNA-lipid nanoparticle (mRNA-LNP) vaccination, Prime and HA. This regimen establishes highly protective HA-specific mucosal immune memory responses in the respiratory tract. Intranasal HA boosters resulted in 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 Immunoglobulin A (IgA) are significantly associated with a reduced viral burden in the upper respiratory tract. Intranasal boosting with bivalent H1 HA induced mucosal immunity against vaccine-matched and mismatched heterologous influenza viruses. Additionally, a heterosubtypic intranasal H5 HA booster elicited H5-reactive mucosal humoral responses in H1-surviving mice. Our work illustrates the potential of a nasal HA protein booster as an adjuvant-free strategy to prevent infection and disease from influenza A viruses.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".