Innovative Mucosal Vaccine Formulations Against Influenza A Virus Infections
Bibliographic record
Abstract
Despite efforts made to develop efficient preventive strategies, infections with influenza A viruses (IAV) continue to cause serious clinical and economic problems. Current licensed human vaccines are mainly inactivated whole virus particles or split-virion administered via the parenteral route. These vaccines provide incomplete protection against IAV in high-risk groups and are poorly/not effective against the constant antigenic drift/shift occurring in circulating strains. Advances in mucosal vaccinology and in the understanding of the protective anti-influenza immune mechanisms suggest that intranasal immunization is a promising strategy to fight against IAV. To date, human mucosal anti-influenza vaccines consist of live attenuated strains administered intranasally, which elicit higher local humoral and cellular immune responses than conventional parenteral vaccines. However, because of inconsistent protective efficacy and safety concerns regarding the use of live viral strains, new vaccine candidates are urgently needed. To prime and induce potent and long-lived protective immune responses, mucosal vaccine formulations need to ensure the immunoavailability and the immunostimulating capacity of the vaccine antigen(s) at the mucosal surfaces, while being minimally reactogenic/toxic. The purpose of this review is to compile innovative delivery/adjuvant systems tested for intranasal administration of inactivated influenza vaccines, including micro/nanosized particulate carriers such as lipid-based particles, virus-like particles and polymers associated or not with immunopotentiatory molecules including microorganism-derived toxins, Toll-like receptor ligands and cytokines. The capacity of these vaccines to trigger specific mucosal and systemic humoral and cellular responses against IAV and their (cross)-protective potential are considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".