Immunogenicity and protective efficacy of an intranasal neuraminidase-based influenza virus vaccine adjuvanted with bacterial cell membrane-derived adjuvants
Bibliographic record
Abstract
Abstract Influenza virus neuraminidase (NA) has emerged as a promising vaccine candidate due to its relatively stable antigenic structure and the ability of NA-specific antibodies to provide cross-protection within influenza virus subtypes. Since the influenza virus causes respiratory infections in humans, developing mucosal vaccines to protect the entry site of the virus is of high importance. Recombinant NA requires adjuvants to induce a protective immune response after mucosal administration. In the current study, we analyze the immunogenicity and protective efficacy of a recombinant NA-based influenza virus vaccine administered intranasally in combination with adjuvants consisting of outer membrane proteins from Neisseria meningitidis complexed with exogenous lipopolysaccharides (LPS) from Shigella flexneri or endogenous LPS from N. meningitidis . We evaluated the local and systemic humoral and cellular immune responses to adjuvanted recombinant N1 NA, analyzing the dynamics of local follicular T-helper (Tfh) cells and germinal center B cells (GCB) in nasal-associated lymphoid tissue (NALT) and tissue-resident memory T cells in lungs, as well as the levels of IgA and IgG in the upper and lower respiratory tracts. Finally, we performed a heterologous challenge study to test the ability of the investigated vaccine formulations to induce cross-protection. The study demonstrates that bacterial cell membrane-derived adjuvants significantly improve the immunogenicity and protective efficacy of the recombinant N1 NA-based influenza vaccine leading to protection against clade 2.3.4.4b H5N1 challenge. This finding supports the potential of these adjuvanted vaccines in providing effective mucosal immunity against influenza virus.
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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) | 0.001 | 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) | 0.000 | 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".