Modulation of injectable hydrogel properties for slow co‐delivery of influenza subunit vaccine components enhance the potency of humoral immunity
Bibliographic record
Abstract
Vaccines are critical for combating infectious diseases across the globe. Influenza, for example, kills roughly 500,000 people annually worldwide, despite annual vaccination campaigns. Efficacious vaccines must elicit a robust and durable antibody response, and poor efficacy often arises from inappropriate temporal control over antigen and adjuvant presentation to the immune system. In this work, we sought to exploit the immune system's natural response to extended pathogen exposure during infection by designing an easily administered slow-delivery influenza vaccine platform. We utilized an injectable and self-healing polymer-nanoparticle (PNP) hydrogel platform to prolong the co-delivery of vaccine components to the immune system. We demonstrated that these hydrogels exhibit unique dynamic physical characteristics whereby physicochemically distinct influenza hemagglutinin antigen and a toll-like receptor 7/8 agonist adjuvant could be co-delivered over prolonged timeframes that were tunable through simple alteration of the gel formulation. We show a relationship between hydrogel physical properties and the resulting immune response to immunization. When administered in mice, hydrogel-based vaccines demonstrated enhancements in the magnitude and duration of humoral immune responses compared to alum, a widely used clinical adjuvant system. We found stiffer hydrogel formulations exhibited slower release and resulted in the greatest improvements to the antibody response while also enabling significant adjuvant dose sparing. In summary, this work introduces a simple and effective vaccine delivery platform that increases the potency and durability of influenza subunit vaccines.
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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 | 0.000 |
| 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".