Mucosal Adjuvants for Influenza Virus-Like Particle Vaccine
Bibliographic record
Abstract
To find an effective mucosal adjuvant for influenza virus-like particles (VLPs), we compared the effects of known adjuvants Alum, CpG DNA, monophosphoryl lipid A (MPL), poly IC, gardiquimod, and cholera toxin (CT). Mice that were intranasally immunized with Alum, CpG, MPL, and CT adjuvanted VLPs showed higher levels of antibodies in both sera and mucosa. Hemagglutination inhibition and virus neutralizing activities were enhanced in groups adjuvanted with Alum, MPL, or CT. Influenza virus specific long-lived cells secreting IgG and IgA antibodies were found at high levels both in bone marrow and spleen in the Alum, CpG and CT adjuvanted groups. A similar level of protection was observed among different adjuvanted groups, except the CT adjuvant that showed a higher efficacy in lowering lung viral loads after challenge. Alum and CT adjuvants differentially increased influenza VLP-mediated activation of dendritic cells and splenocytes in vitro, supporting the in vivo pattern of antibody isotypes and cytokine production. These results suggest that Alum, MPL, or CpG adjuvants, which have been tested clinically, can be developed as an effective mucosal adjuvant for influenza VLP vaccines.
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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".