Immunity and protection following an H5N1-based polyanhydride nanoparticle IAV vaccine 4152
Bibliographic record
Abstract
Abstract Description Highly pathogenic avian influenza (HPAI) H5N1 influenza virus (IAV) has led to numerous outbreaks in animals and more recently humans within the US. Currently, IAV vaccines are designed to generate antibodies against strain-specific IAV hemagglutinin. In contrast, the induction of T cell immunity after current IAV vaccines is minimal or limited. Importantly, existing memory T cells are known to be important in protection against heterologous IAV infections. Our prior work has shown that our polyanhydride IAV vaccine (IAV-nanovax), in contrast to existing IAV vaccines, induces robust T and B cell immunity and protection against homologous and heterologous IAV. Therefore, we examined the immunity and protection afforded by a new H5N1 based nanoparticle vaccine (i.e. H5-nanovax) in comparison to our H1N1 based vaccine (i.e. IAV-nanovax H1). Our findings show that in contrast to naïve mice, mice administered the H5-nanovax were protected from morbidity and mortality when challenged with either the HPAI and low path forms of H5N1. Interestingly, H1-nanovax also reduced morbidity and mortality after H5 IAV challenge. The protection observed is consistent with our observation of local and systemic immune responses (T cell and B cell) as well as antibodies against IAV after vaccination and before virus challenge. Thus, due to its T cell induction, IAV-nanovax may hold the potential to offer broad-based cross-strain protection against both seasonal and potential pandemic IAV. Funding Sources NIH/NIAID R01AI168001; NIH/NIAID R01AI127565; NIH/NIAID R01AI141196 Topic Categories Vaccines and Immunotherapy (VAC)
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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) | 0.001 | 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".