Intranasal immunization of mice with a formalin-inactivated bovine respiratory syncytial virus vaccine co-formulated with CpG oligodeoxynucleotides and polyphosphazenes results in enhanced protection
Bibliographic record
Abstract
As respiratory syncytial virus (RSV) targets the mucosal surfaces of the respiratory tract, induction of both systemic and mucosal immunity will be critical for optimal protection. In this study, the ability of an intranasally delivered, formalin-inactivated bovine RSV (FI-BRSV) vaccine co-formulated with CpG oligodeoxynucleotides (ODN) and polyphosphazenes (PP) to induce systemic and mucosal immunity, as well as protection from BRSV challenge, was evaluated. Intranasal immunization of mice with FI-BRSV formulated with CpG ODN and PP resulted in both humoral and cell-mediated immunity, characterized by enhanced production of BRSV-specific serum IgG, as well as increased gamma interferon and decreased interleukin-5 production by in vitro-restimulated splenocytes. These mice also developed mucosal immune responses, as was evident from increased production of BRSV-specific IgG and IgA in lung-fragment cultures. Indeed, the increases in serum and mucosal IgG, and in particular mucosal IgA and virus-neutralizing antibodies, were the most critical differences observed between FI-BRSV formulated with both CpG ODN and PP in comparison to formulations with CpG ODN, non-CpG ODN or PP individually. Finally, FI-BRSV/CpG/PP was the only formulation that resulted in a significant reduction in viral replication upon BRSV challenge. Co-formulation of CpG ODN and PP is a promising new vaccine platform technology that may have applications in mucosal immunization in humans.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".