Synthetic Multicomponent Nanovaccines Based on the Molecular Co-assembly of β-Peptides Protect against Influenza A Virus
Bibliographic record
Abstract
Peptides with the ability to self-assemble into nanoparticles have emerged as an attractive strategy to design antigen delivery platforms for subunit vaccines. While toll-like receptor (TLR) agonists are promising immunostimulants, their use as soluble agents is limited by their rapid clearance and off-target inflammation. Herein, we harnessed molecular co-assembly to prepare multicomponent cross-β-sheet peptide nanofilaments exposing an antigenic epitope derived from the influenza A virus and a TLR agonist. The TLR7 agonist imiquimod and the TLR9 agonist CpG were respectively functionalized on the assemblies by means of an orthogonal pre- or post-assembly conjugation strategy. The nanofilaments were readily uptaken by dendritic cells, and the TLR agonists retained their activity. Multicomponent nanovaccines induced a robust epitope-specific immune response and completely protected immunized mice from a lethal influenza A virus inoculation. This versatile bottom-up approach is promising for the preparation of synthetic vaccines with customized magnitude and polarization of the immune responses.
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) | 0.000 | 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".