The effectiveness and immunogenicity of an oil-in-water nano-emulsion protein subunit vaccine against Pseudomonas aeruginosa in diet induced obese mice 2193
Bibliographic record
Abstract
Abstract Description Pseudomonas aeruginosa (Pa) is an opportunistic pathogen known to cause severe infections of upper and/or lower respiratory tracts in immunocompromised individuals. With 13% of the world’s population being obese, infections and immunesenescence are on the rise. Moreover, increased fitness of Pa is conferred by the absence of licensed vaccines, rising antibiotic resistance to name a few. We showed LTA-1 fused type three secretion system associated proteins of Pa, PcrV and PopB (L-PaF) in oil-in-water nano-emulsion ME to be protective in young and elderly mice in presence and absence of TLR agonist BECC438b. Here, we extended our work in the realm of obese mice where dose-escalation studies using L-PaF were found to be reducing the onset of infection. Optimum concentration of the immunogen was established by checking humoral, cellular responses before and after immunization and lung burden post infection. Correlates of protection were established in terms of presence of antibodies with high opsonophagocytic activity, IL-17A and IFN-g in spleens and IL-22, IL-17A and IL-2 in lungs before challenge. Challenge with mucoid Pa clinical isolate, however, changes the cytokine signature into a more pro-inflammatory type in lungs, compared to spleen in control vs immunized mice. This work addresses a serious health risk and shows a way to prevent it. Funding Sources This work was funded by the U.S. Department of Health & Human Services (NIH) National Institute of Allergy and Infectious Diseases (NIAID) to WLP (grant number: R01AI169781), and by the U.S. Department of Health & Human Services (NIH) National Institute of Allergy and Infectious Diseases (NIAID) to RKE (contract number: HHSN272201800043C). 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 | 0.001 | 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".