Inverted HA-EV immunization elicits stalk-specific influenza immunity and cross-protection in mice
Bibliographic record
Abstract
Enhancing protective immunity in the respiratory tract is crucial to combat influenza infection and transmission. Developing mucosal universal influenza vaccines requires effective delivery platforms to overcome the respiratory mucosal barrier and stimulate appropriate innate immune reactions, thereby bridging adaptive immune responses with minimal necessary inflammation. Meanwhile, the vaccine platforms must be biocompatible. This study employed cell-derived extracellular vesicles (EVs) as a mucosal universal influenza vaccine platform. By conjugating influenza hemagglutinin (HA) onto EV surfaces through HA-receptor interaction, we achieved an upside-down (inverted) influenza HA configuration that exposed the conserved HA stalk region while partially hiding the globular head domain. Intranasal immunization with the resulting EVs induced robust HA stalk- and virus-specific serum antibody and mucosal immune responses in mice, protecting against heterologous virus infection. Notably, EVs derived from the lung epithelial cell line A549 induced superior cross-reactive antibodies and enhanced protection upon intranasal immunization. EVs conjugating multivalent HA elicited broadly cross-reactive antibody and cellular responses against different influenza strains. Our results demonstrated that EVs conjugating multiple inverted HAs represented an effective strategy for developing a mucosal universal influenza vaccine.
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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 | 0.001 | 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".