Intranasal Immunization with an Archaeal Lipid Mucosal Vaccine Adjuvant and Delivery Formulation Protects against a Respiratory Pathogen Challenge
Bibliographic record
Abstract
Archaeal lipid mucosal vaccine adjuvant and delivery (AMVAD) is a safe mucosal adjuvant that elicits long lasting and memory boostable mucosal and systemic immune responses to model antigens such as ovalbumin. In this study, we evaluated the potential of the AMVAD system for eliciting protective immunity against mucosal bacterial infections, using a mouse model of intranasal Francisella tularensis LVS (LVS) challenge. Intranasal immunization of mice with cell free extract of LVS (LVSCE) adjuvanted with the AMVAD system (LVSCE/AMVAD) induced F. tularensis-specific antibody responses in sera and bronchoalveolar lavage fluids, as well as antigen-specific splenocyte proliferation and IL-17 production. More importantly, the AMVAD vaccine partially protected the mice against a lethal intranasal challenge with LVS. Compared to LVSCE immunized and naïve mice, the LVSCE/AMVAD immunized mice showed substantial to significant reduction in pathogen burdens in the lungs and spleens, reduced serum and pulmonary levels of proinflammatory cytokines/chemokines, and longer mean time to death as well as significantly higher survival rates (p<0.05). These results suggest that the AMVAD system is a promising mucosal adjuvant and vaccine delivery technology, and should be explored further for its applications in combating mucosal infectious diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".