Mucosal delivery of a polyanhydride nanovaccine incorporating CpG and the post-fusion F/ G proteins induces protective immunity against BRSV infection in neonatal calves.
Bibliographic record
Abstract
Abstract Objective: Previously, immunization with a single intranasal dose of a BRSV post-fusion F/G nanovaccine resulted in partial protection from a BRSV challenge (McGill et al. 2019, McGill et al. 2018). Knowledge gained from these previous studies was used to optimize the vaccine by incorporating CpG-ODN and to investigate the impact of mucosal vs. systemic vaccination routes. In this study a prime-boost immunization regimen was used. Method: 36 neonatal, mixed-sex, Holstein calves were divided into 6 groups. Group 1 received a heterologous regime with saline and group 2 a homologous boost of the ‘empty’ nanovaccine CPG. Groups 3 and 4 received a homologous mucosal regime of the nanovaccine ± CpG, while groups 5 and 6 received a heterologous regime of the nanovaccine ± CpG. Vaccine-induced responses were monitored and 6 weeks after boost, all calves were challenged with BRSV. Nasopharyngeal swabs were collected for viral shedding and calves monitored for clinical signs and euthanized on day 7 after infection. Lungs were scored for gross pathology. Blood, nasal secretions and lung tissue samples were analyzed for BRSV specific immune responses. Results: Unvaccinated controls and CpG-only nanovaccine controls developed lung pathology consistent with a severe BRSV infection. We observed no evidence of vaccine enhanced disease in any vaccinated group. Calves that received the homologous vaccination of the nanovaccine + CPG had significantly less lung pathology and viral burden in the lungs compared to control calves. Conclusions: These results indicate that an intranasal homologous vaccination regimen with the post-fusion F/G + CpG nanovaccine has the capacity to reduce BRSV disease in neonatal calves with preexisting maternal antibodies.
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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".