ISCOMs/MPLA‐Adjuvanted SDAD Protein Nanoparticles Induce Improved Mucosal Immune Responses and Cross‐Protection in Mice
Bibliographic record
Abstract
Abstract The epidemics caused by the influenza virus are a serious threat to public health and the economy. Adding appropriate adjuvants to improve immunogenicity and finding effective mucosal vaccines to combat respiratory infection at the portal of virus entry are important strategies to boost protection. In this study, a novel type of core/shell protein nanoparticle consisting of influenza nucleoprotein (NP) as the core and NA1‐M2e or NA2‐M2e fusion proteins as the coating antigens by SDAD hetero‐bifunctional crosslinking is exploited. Immune‐stimulating complexes (ISCOMs)/monophosphoryl lipid A (MPLA) adjuvants further boost the NP/NA‐M2e SDAD protein nanoparticle‐induced immune responses when administered intramuscularly. The ISCOMs/MPLA‐adjuvanted protein nanoparticles are delivered through the intranasal route to validate the application as mucosal vaccines. ISCOMs/MPLA‐adjuvanted nanoparticles induce significantly strengthened antigen‐specific antibody responses, cytokine‐secreting splenocytes in the systemic compartment, and higher levels of antigen‐specific IgA and IgG in the local mucosa. Meanwhile, significantly expanded lung resident memory (RM) T and B cells (T RM /B RM ) and alveolar macrophages population are observed in ISCOMs/MPLA‐adjuvanted nanoparticle‐immunized mice with a 100% survival rate after homogeneous and heterogeneous H3N2 viral challenges. Taken together, ISCOMs/MPLA‐adjuvanted protein nanoparticles could improve strong systemic and mucosal immune responses conferring protection in different immunization routes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".