Respiratory syncytial virus-like nanoparticle vaccination induces long-term protection without pulmonary disease by modulating cytokines and T-cells partially through alveolar macrophages
Bibliographic record
Abstract
The mechanisms of protection against respiratory syncytial virus (RSV) are poorly understood. Virus-like nanoparticles expressing RSV glycoproteins (eg, a combination of fusion and glycoprotein virus-like nanoparticles [FG VLPs]) have been suggested to be a promising RSV vaccine candidate. To understand the roles of alveolar macrophages (AMs) in inducing long-term protection, mice that were 12 months earlier vaccinated with formalin-inactivated RSV (FI-RSV) or FG VLPs were treated with clodronate liposome prior to RSV infection. FI-RSV immune mice with clodronate liposome treatment showed increases in eosinophils, plasmacytoid dendritic cells, interleukin (IL)-4(+) T-cell infiltration, proinflammatory cytokines, chemokines, and, in particular, mucus production upon RSV infection. In contrast to FI-RSV immune mice with severe pulmonary histopathology, FG VLP immune mice showed no overt sign of histopathology and significantly lower levels of eosinophils, T-cell infiltration, and inflammatory cytokines, but higher levels of interferon-γ, which are correlated with protection against RSV disease. FG VLP immune mice with depletion of AMs showed increases in inflammatory cytokines and chemokines, as well as eosinophils. The results in this study suggest that FG nanoparticle vaccination induces long-term protection against RSV and that AMs play a role in the RSV protection by modulating eosinophilia, mucus production, inflammatory cytokines, and T-cell infiltration.
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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".