Mucosal co-immunization with AIM2 enhances protective SIgA response and increases prophylactic efficacy of chitosan-DNA vaccine against coxsackievirus B3-induced myocarditis
Bibliographic record
Abstract
Coxsackievirus B3 (CVB3) infection is considered as the most common cause of viral myocarditis with no available vaccine. Considering that CVB3 mainly invades through the gastrointestinal mucosa, the development of CVB3-specific mucosal vaccine, which is the most efficient way to induce mucosal immune responses, gains more and more attention. In this study, we used absent in melanoma 2 (AIM2) as a mucosal adjuvant to enhance the immunogenicity and immunoprotection of CVB3-specific chitosan-pVP1 vaccine. Mice were intranasally co-immunized with 50 μg chitosan-pAIM2 and equal amount of chitosan-pVP1 vaccine 4 times at 2 week-intervals, and then challenged with CVB3 2 weeks after the last immunization. Compared with chitosan-pVP1 vaccine immunization alone, chitosan-pAIM2 co-immunization enhanced resistance to CVB3-induced myocarditis evidenced by significantly enhanced ejection fractions from 55.40 ± 9.35 to 80.31 ± 11.35, improved myocarditis scores from 1.50 ± 0.45 to 0.30 ± 0.15, reduced viral load from 3.33 ± 0.50 to 0.50 ± 0.65, and increased survival rate from 40.0% to 75.5%. This increased immunoprotection might be attributed to the augmented level of CVB3-specific fecal SIgA with high affinity and neutralizing ability. In addition, co-immunization with chitosan-pAIM2 remarkably facilitated dendritic cells (DCs) recruitment to mesenteric lymph nodes (MLN), and promoted the expression of IgA-inducing factors (BAFF, APRIL, iNOS, RALDH1, IL-6, TGF-β), which might account for its mucosal adjuvant effect. This strategy may represent a promising prophylactic vaccine against CVB3-induced myocarditis.
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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".